Siraj Raval

Table of Contents

https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A/playlists

1 AI A to Z

1.1 Artificial Curiosity

http://www.youtube.com/watch?v=aom4RMOHezc

Curiosity is something that all humans exhibit in some way throughout their lives. Recently, a team at Berkeley published a paper on Curiosity driven learning, and they demonstrated how it helped enable their AI agent to learn how to play the popular game Super Mario Brother very efficiently with the added benefit of curiosity to help Mario explore his options. I'll explain how it works in this video using code, animations, math, and the spoken word. This technology can be used to help make our systems more intelligent, and thus our applications more capable of helping other people. Enjoy!

Code for this video:
https://github.com/llSourcell/noreward-rl

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This video is apart of the Move 37 course at School of AI:
https://www.theschool.ai

More learning resources:
https://pathak22.github.io/large-scale-curiosity/
https://pathak22.github.io/large-scale-curiosity/resources/largeScaleCuriosity2018.pdf
https://alumni.berkeley.edu/california-magazine/winter-2017-power/super-curious-mario-teaching-ai-keep-asking-questions
https://www.technologyreview.com/s/607886/curiosity-may-be-vital-for-truly-smart-ai/
https://www.youtube.com/watch?v=0Ey02HT_1Ho

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#ArtificialCuriosity #SirajRaval #AI
Hiring? Need a Job? See our job board!:
www.theschool.ai/jobs/

Need help on a project? See our consulting group:
www.theschool.ai/consulting-group/

2 Move 37

2.1 Move 37 Teaser Trailer

http://www.youtube.com/watch?v=Xr8lrBAfHcA

Move 37 is the name of my next course. It will be free, open-source, and will cover reinforcement learning from the basics to modern-day techniques. The pioneers of AI were ambitious dreamers who were laser focused on reverse engineering the complexities of human intelligence to harness it as a tool to benefit humanity. Reinforcement learning is behind the latest advances in AI, and will be a crucial piece to the puzzle of intelligence. I'll update you all more soon, right now I just wanted to start the hype train. Enjoy!

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Twitter: https://twitter.com/sirajraval
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Visit the School of AI:
https://www.theschool.ai/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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2.2 Move 37 Official Trailer

http://www.youtube.com/watch?v=Ei1YBf6qQAw

Join me as I teach this, free 10 week reinforcement learning course I’ve called Move 37. I’ll take you on a journey through the basics up to modern day techniques. Every week, we’ll build apps together that will cover both toy and industry problems. You’ll be able to measure your progress along the way by chatting with your peers both online and offline at School of AI chapters globally, taking quizzes, coding challenges, and 2 graded projects. I’ll have weekly coding live streams to help answer any questions, and my assistant instructors will be available to help in our community slack channel. All Wizards who complete the course get an official School of AI Certificate, signed by me. This is going to be wild ride, Signup now and join the movement!

Course Signup Page (Starts September 10th):
https://www.theschool.ai/courses/move-37

Github Syllabus:
https://github.com/llSourcell/Move_37_Syllabus

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Yes all of my videos will continue to be released on Youtube, every single one. The course website is just so that i can offer non-video content like quizzes, assignments, etc.

My Playlists:
https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A/playlists

Related Videos:
https://www.youtube.com/watch?v=i_McNBDP9Qs&list=PL2-dafEMk2A5FZ-MnPMpp3PBtZcINKwLA

Visit School of AI:
https://www.theschool.ai/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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2.3 Introduction (Move 37)

http://www.youtube.com/watch?v=fRmZck1Dakc

Welcome to my new reinforcement learning course titled Move 37! in this 10 week course we'll go over the basics of reinforcement learning up to modern day techniques that involve neural networks called 'deep' reinforcement learning. In this first video, i'll introduce the idea of a Markov Decision Process. This is the basic mathematical framework for framing the reinforcement learning problem. We'll also briefly mention the ideas of a 'policy' and the agent-environment loop. Get hype!

Code for this video:
https://github.com/llSourcell/Introduction_Move37

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The School of AI:
https://www.theschool.ai

Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology
instagram: https://www.instagram.com/sirajraval

Github Syllabus:
https://github.com/llSourcell/Move_37_Syllabus

More learning resources:
https://towardsdatascience.com/reinforcement-learning-demystified-markov-decision-processes-part-1-bf00dda41690
https://www.cs.rice.edu/~vardi/dag01/givan1.pdf
http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MDP.pdf
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-825-techniques-in-artificial-intelligence-sma-5504-fall-2002/lecture-notes/Lecture20FinalPart1.pdf
https://artint.info/html/ArtInt_224.html

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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2.4 Sensor Networks

http://www.youtube.com/watch?v=PYQAI6Td2wo

What is the best way to route data in a network of routers spread out across the globe? This 'internet of things'-based problem can be solved using reinforcement learning! In this video, i'll explain the 2 types of policies, the bellman equation, and the value function. All of these concepts are crucial in the RL pipeline and using animations + code, i'll break them down. Enjoy!

Code for this video:
https://github.com/llSourcell/Sensor_Networks

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Github Syllabus:
https://github.com/llSourcell/Move_37_Syllabus

Take the full course at the School of AI:
https://www.theschool.ai

More learning resources:
https://becominghuman.ai/the-very-basics-of-reinforcement-learning-154f28a79071
https://medium.freecodecamp.org/an-introduction-to-reinforcement-learning-4339519de419
https://www.oreilly.com/ideas/reinforcement-learning-explained
http://kvfrans.com/reinforcement-learning-basics/
https://medium.com/syncedreview/basics-of-computational-reinforcement-learning-fca09f3609ea
https://www.toptal.com/machine-learning/deep-dive-into-reinforcement-learning
http://www.wildml.com/2016/10/learning-reinforcement-learning/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693

#SensorNetworks #SirajRaval
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2.5 Google Dopamine (LIVE)

http://www.youtube.com/watch?v=FWFoyFjeAaM

Google recently released a research framework for fast prototyping of reinforcement learning algorithms called "Dopamine". They say that it aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research). In this live stream i'll test it out, try out a few basic reinforcement learning algorithms and compare it to similar frameworks. You can code along with me using CoLab. Get hype!

Code for this video:
https://github.com/llSourcell/Google_Dopamine_LIVE

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The School of AI:
https://www.theschool.ai

Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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instagram: https://www.instagram.com/sirajraval

Github Syllabus:
https://github.com/llSourcell/Move_37_Syllabus

More learning resources:
https://github.com/google/dopamine
https://ai.googleblog.com/2018/08/introducing-new-framework-for-flexible.html
https://joshgreaves.com/reinforcement-learning/understanding-rl-the-bellman-equations/
https://www.youtube.com/watch?v=N0Ld2iTMaMs

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w
Hiring? Need a Job? See our job board!:
www.theschool.ai/jobs/

Need help on a project? See our consulting group:
www.theschool.ai/consulting-group/

3 School of AI

3.1 The School of AI (Teaser Trailer)

http://www.youtube.com/watch?v=ARy91XqIWpk

This is a teaser trailer for The School of AI.

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3.2 School of AI Introduction

http://www.youtube.com/watch?v=8yu8rtXThy8

We are School of AI! Our mission is to offer a world-class AI education to anyone on Earth for free. Our doors are open to all those who wish to learn. We are a learning community that spans almost every country dedicated to teaching our students how to make a positive impact in the world using AI technology, whether that's through employment or entrepreneurship. In this video, I make a request for applications to signup as a School of AI Dean for your local city. Deans host learning meetups and are help guide students along their learning journey.

Signup here:
https://docs.google.com/forms/d/e/1FAIpQLSdwbOGrPAvmFtohYO1QWxojk_77rEsc1oJSSMGZPtLCG1b8-Q/viewform?usp=pp_url

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Twitter: https://twitter.com/sirajraval
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We have our first School of AI merchandise as well:
https://teespring.com/school-of-ai

This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources:
https://www.youtube.com/watch?v=vOppzHpvTiQ
https://www.youtube.com/watch?v=T5pRlIbr6gg
https://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D
https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Sign up for the next course at The School of AI:
https://www.theschool.ai

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w

3.3 School of AI Cities

http://www.youtube.com/watch?v=_7TvMA_w8xw

Over 800 Deans now represent School of AI Chapters across 400 cities globally! We are an international, nonprofit school dedicated to studying, teaching, and creating Artificial Intelligence to help solve the world’s most difficult problems. Deans are guardians of our mission - “To offer a world-class AI education to anyone on Earth for free." and our core values. In this video, I'll describe the work they've done so far, then show you how you can find your nearest School of AI Chapter. Enjoy!

Attend your nearest School of AI Chapter using this map:
https://bit.ly/2wldw7s

Is your city not listed? See our more comprehensive, raw list:
https://docs.google.com/spreadsheets/d/1YTUeyJbf3S-75mKYlo8MMlEes-QDrDk7dmrbAfrkKmY/edit?usp=sharing

Still can't find one? Ask one of our Deans in our Slack community:
http://wizards.herokuapp.com/

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Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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Here is yet another list of School of AI Chapters:
https://www.google.com/maps/d/u/0/viewer?mid=1fmlKwZZXGoNvZnbZlnPazvTcPcJ89nno&ll=27.620243622926218%2C40.21712610000009&z=2

Sign up for my upcoming course, details TBD:
https://www.theschool.ai/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693

#SirajRaval #SchoolOfAI
Signup for my newsletter for exciting updates in the field of AI:
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3.4 100 Days of ML Code Challenge

http://www.youtube.com/watch?v=cuQMBj1cWPo

Who’s ready to take the 100 days of ML code challenge? That means coding and/or studying machine learning for at least an hour everyday for the next 100 days. Pledge with the #100DaysOfMLCode hashtag on your social media platform of choice. I’ll give the first few winners a shoutout!

Instructions here:
https://github.com/llSourcell/100_Days_of_ML_Code

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There are only 3 rules.
1 Make a public pledge using the #100DaysofMLCode hashtag on your favorite social media platform.
2 Make a public log of your work that you update daily, you can do this via GitHub a blog or a vlog.
3. if you see someone make a post using the #100DaysofMLCode hashtag, give it a like share or comment.

Project Idea:
https://www.drivendata.org/competitions/44/dengai-predicting-disease-spread/

Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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instagram: https://www.instagram.com/sirajraval

This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Sign up for the next course at The School of AI:
https://www.theschool.ai

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w

3.5 Learn Machine Learning in 3 Months (with curriculum)

http://www.youtube.com/watch?v=Cr6VqTRO1v0

How is a total beginner supposed to get started learning machine learning? I'm going to describe a 3 month curriculum to help you go from beginner to well-versed in machine learning. Its an accelerated learning plan, something i'd create for myself if I were to get started today, but I'm going to open source it for you guys. This curriculum will cover all the math concepts, the machine learning theory, and the deep learning theory to get you up to speed with the field as fast as possible. If anyone asks how to best get started with machine learning, direct them to this video!

Curriculum from this video:
https://github.com/llSourcell/Learn_Machine_Learning_in_3_Months

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Month 1

Week 1 Linear Algebra
https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
Week 2 Calculus
https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr
Week 3
https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2
Week 4 Algorithms
https://www.coursera.org/courses?languages=en&query=Algorithm%20design%20and%20analysis

Month 2

Week 1
learn python for data science
https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU
Math of Intelligence
https://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D
Intro to Tensorflow
https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV

Week 2
Intro to ML (Udacity)
https://eu.udacity.com/course/intro-to-machine-learning--ud120

Week 3-4
ML Project Ideas
https://github.com/NirantK/awesome-project-ideas

Month 3 (Deep Learning)

Week 1
Intro to Deep Learning
https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3

Week 2
Deep Learning by Fast.AI
http://course.fast.ai/

Week 3-4
Re-implement DL projects from my github
https://github.com/llSourcell?tab=repositories

ML people to follow on Twitter:
https://www.quora.com/Who-should-I-follow-on-Twitter-to-get-useful-and-reliable-machine-learning-information


Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693
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https://goo.gl/FZzJ5w

3.6 How to Study Machine Learning

http://www.youtube.com/watch?v=waXHrc2m9K8

Let me show you the techniques I use to study machine learning in this video. That includes living a healthy lifestyles, optimizing your learning environment, creating a personalized learning path, prioritizing effectively, and being an active learner. I'll demo the FAST technique, which you can use to help learn faster and more efficiently. I made this with machine learning technology in mind, but these techniques can be used for any field. Enjoy!

Please Subscribe! And like. And comment. That's what keeps me going.

Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
instagram: https://www.instagram.com/sirajraval
Facebook: https://www.facebook.com/sirajology

This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources:
http://jimkwik.com/kwik-brain-001/
https://github.com/llSourcell/Learn_Deep_Learning_in_6_Weeks
https://hbr.org/2018/07/take-control-of-your-learning-at-work
https://www.youtube.com/watch?v=nxWfZP6eslM
https://www.youtube.com/watch?v=YzfdL58virc
https://www.youtube.com/watch?v=cuQMBj1cWPo&t=7s

Here is a v1 list of School of AI Chapters (cleaner list coming ASAP):
https://www.google.com/maps/d/u/0/viewer?mid=1fmlKwZZXGoNvZnbZlnPazvTcPcJ89nno&ll=27.620243622926218%2C40.21712610000009&z=2

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693

#SirajRaval #HowTo #MachineLearning
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w

3.7 How to Prevent an AI Apocalypse

http://www.youtube.com/watch?v=fLWnCjOvcwg

I traveled to Amsterdam for a week to speak at The Next Web Conference on AI Safety. While roaming the streets of the city, I decided to take some shots and formulate a video on the same topic for you guys. In the battle of good vs evil, it's up to our community to ensure good wins. I'll resume the coding videos next week when I get back to San Francisco.

Please Subscribe! And like. And comment. That's what keeps me going.

I'll post a link to the talk once it's up, here's an article in the mean time:
https://thenextweb.com/artificial-intelligence/2017/05/18/how-to-keep-ai-from-killing-us-all/#.tnw_VaEi7vjZ

More Learning resources:
https://futureoflife.org/ai-safety-research/
https://iamtrask.github.io/2017/03/17/safe-ai/
https://blog.openai.com/concrete-ai-safety-problems/
https://intelligence.org/why-ai-safety/
https://80000hours.org/career-reviews/artificial-intelligence-risk-research/
https://foundational-research.org/files/suffering-focused-ai-safety.pdf

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http://wizards.herokuapp.com/

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3.8 How to Use GitHub

http://www.youtube.com/watch?v=Loav1kbA640

GitHub is the most popular platform for developers across the world to share and collaborate on programming projects together. In this video, i'll explain how the git protocol works using the analogy of a car company (Tesla), how github works, and then show you from command line how you can push your first repository to github on your own computer.

Git commands in this video:
https://github.com/llSourcell/How-to-Use-GitHub

Please Subscribe! And like. And comment. That's what keeps me going.

Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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instagram: https://www.instagram.com/sirajraval

This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More learning resources:
https://guides.github.com/activities/hello-world/
https://product.hubspot.com/blog/git-and-github-tutorial-for-beginners
https://try.github.io/
http://kbroman.org/github_tutorial/
https://blog.udacity.com/2015/06/a-beginners-git-github-tutorial.html

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Sign up for the next course at The School of AI:
https://www.theschool.ai

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w

3.9 How to Learn Advanced Concepts Fast

http://www.youtube.com/watch?v=nxWfZP6eslM

These are 10 strategies I use to learn advanced concepts as fast as possible! I'm going to explain each one, and give some examples of what I mean. No coding challenge this week, hope its helpful!

10 Strategies below:

1. Find a reason to learn
2. Start with the simplest explanations
3. Create a set of small, achievable goals
4. Set Deadlines
5. Maintain a flow state
6. Let your curiosity guide your learning path
7. Spend 1/3 of your time researching & 2/3 doing
8. Take notes by hand
9. Dont multitask
10. Maintain your Health

(yes, i mis-numbered some of them in the video accidentally)

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Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Follow me:
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3.10 How to Read a Research Paper

http://www.youtube.com/watch?v=SHTOI0KtZnU

Ever wondered how I consume research so fast? I'm going to describe the process i use to read lots of machine learning research papers fast and efficiently. It's basically a 3-pass approach, i'll go over the details and show you the extra resources I use to learn these advanced topics. You don't have to be a PhD, anyone can read research papers. It just takes practice and patience.

Please Subscribe! And like. And comment. That's what keeps me going.

Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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instagram: https://www.instagram.com/sirajraval

More learning resources:
http://www.arxiv-sanity.com/
https://www.reddit.com/r/MachineLearning/
https://www.elsevier.com/connect/infographic-how-to-read-a-scientific-paper
https://www.quora.com/How-do-I-start-reading-research-papers-on-Machine-Learning
https://www.reddit.com/r/MachineLearning/comments/6rj9r4/d_how_do_you_read_mathheavy_machine_learning/
https://machinelearningmastery.com/how-to-research-a-machine-learning-algorithm/
http://www.sciencemag.org/careers/2016/03/how-seriously-read-scientific-paper

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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3.11 How to Overcome Failure

http://www.youtube.com/watch?v=kOLSDsjeSIE

Everyone fails. In this video, i'll recount 5 times in my life where I failed and talk about how I recovered. Whether it be in work life, school life, or personal life, failure is just a reality of life. Its how you deal with it that defines your future. If you're wondering, I recorded this is Lisbon Portugal since I was invited to speak at a Data Science meetup about blockchain AI. I took this is my airbnb when i had some free time.

Hammad's Winning Code:
https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/blob/master/Binary%20Logistic%20Regression/Binary%20Logistic%20Regression.ipynb

Wladi's Runner up code:
https://github.com/wladiarce/logistic_regression_numpy

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More learning resources:
https://simpleprogrammer.com/overcoming-obstacles-stoic-mindset/
https://blog.todoist.com/2015/04/14/overcome-fear-of-failure/
https://www.quora.com/How-can-I-overcome-the-fear-of-failure-especially-fear-of-coding
https://thenextweb.com/dd/2015/06/11/8-barriers-to-overcome-when-learning-to-code/

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http://wizards.herokuapp.com/

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3.12 How to Teach AI

http://www.youtube.com/watch?v=tczjZOLVjJM

Teaching AI is the best way to learn AI. In this video, I'll cover 3 very important topics - how to design an AI curriculum, teaching IRL best practices, and how to create an educational AI youtube video. Tools that we'll use include GitHub, final cut pro, the charisma on command youtube channel, stackoverflow, reddit, and a few others. I hope you find this video useful! I created this video to both help current School of AI Deans create better learning paths for their communities, and to help all Wizards in their journey to master this field. Enjoy!

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

My playlists:
https://www.youtube.com/results?search_query=siraj+raval+playlists

Example AI curriculum:
https://github.com/llSourcell/Learn_Machine_Learning_in_3_Months

Kaggle Kernels (Great Intro to AI for Beginner Workshops):
https://www.kaggle.com/kernels

Another amazing intro to AI tool for workshops:
https://github.com/tensorflow/lucid#notebooks

Coursera CNN course:
https://www.coursera.org/learn/convolutional-neural-networks

Stanford CNN Course:
http://cs231n.stanford.edu/

Carykh's channel:
https://www.youtube.com/user/carykh/videos

Charisma on Command:
https://www.youtube.com/user/charismaoncommand

Free Learning Management Systems:
https://blog.capterra.com/top-8-freeopen-source-lmss/

Learn after effects for animations free:
https://www.youtube.com/playlist?list=PLUMFUmbeXFQZ0a-qkeEgx1btFZzAFFfAv

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Learn more about the School of AI:
https://www.theschool.ai

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#SirajRaval #HowTo #AI
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3.13 Beautiful Mathematics in the Natural World

http://www.youtube.com/watch?v=b3kA3Yn5VWY

This is my end of the year video! I wanted to do something a little different. I traveled to Portland, Oregon for a week to explore/meet people and decided to use the shots I took while hiking to illustrate how math is all around us in the natural world. We can and will discover the rules of intelligence. The fact that it is governed by mathematics only makes it that much more beautiful. From simple rules emerge incredible complexity.

Vishal's Winning code:
https://github.com/erilyth/visualize_dataset_demo

Sethu's runner up code:
https://github.com/sethuiyer/visualize-GOT

Original peer-reviewed paper in Science mag by Cambridge Professor Stolum (cited 221 times) on how the average sinuosity of all rivers is pi:
http://raaf.org/pdfs/meandering_river.pdf

More Learning Resources:
https://www.comsol.com/multiphysics/navier-stokes-equations
https://www.theguardian.com/science/2016/nov/21/magic-numbers-can-maths-equations-be-beautiful
https://westhunt.wordpress.com/2013/06/07/the-breeders-equation/
https://www.youtube.com/watch?v=GzCvlFRISIM

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http://wizards.herokuapp.com/

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The background music is the Interstellar theme by Hans Zimmer
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3.14 Research to Code - Machine Learning tutorial

http://www.youtube.com/watch?v=pQyzdwHBbqo

A lot of times, research papers don't have an associated codebase that you can browse and run yourself. In cases like that, you'll have to code up the paper yourself. That is easier said than done, and in this video i'll show you how you should read and dissect a research paper so you can quickly implement it programmatically. The paper we'll be implementing in this video is called Neural Style transfer, that applies artistic filters to an image using 3 loss functions. Its a great starting point, i'll demo it using code, animations, and math. Enjoy!

Code for this video:
https://github.com/llSourcell/Research_to_Code


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github + code website is:
http://www.gitxiv.com/

More learning resources;
https://www.youtube.com/watch?v=-mu3TYZ_udM&t=2s
https://www.youtube.com/watch?v=SHTOI0KtZnU
https://medium.com/artists-and-machine-intelligence/neural-artistic-style-transfer-a-comprehensive-look-f54d8649c199
https://github.com/anishathalye/neural-style

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3.15 Resume for Machine Learning

http://www.youtube.com/watch?v=nMK94JlKRb4

Welcome to my new course, Machine Learning Journey! If you’re a student, or between jobs, or in a different field, this 10 week course will help you learn everything you need from marketing your skills to building a solid mathematical foundation in order to get a job or start your own venture as a machine learning engineer or data scientist. I'm going to show you how to write a great resume in this first video. There are some key things to keep in mind and it depends on the company you're applying to. I'll cover it all, enjoy!

Curriculum for this course:
https://github.com/llSourcell/Machine_Learning_Journey

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More learning resources:
lhttps://www.linkedin.com/in/sirajraval/
https://www.theschool.ai/pages/jobs?p=1
https://novoresume.com/
https://www.wordclouds.com/
https://www.topresume.com/

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4 Machine Learning Journey

4.1 Resume for Machine Learning

http://www.youtube.com/watch?v=nMK94JlKRb4

Welcome to my new course, Machine Learning Journey! If you’re a student, or between jobs, or in a different field, this 10 week course will help you learn everything you need from marketing your skills to building a solid mathematical foundation in order to get a job or start your own venture as a machine learning engineer or data scientist. I'm going to show you how to write a great resume in this first video. There are some key things to keep in mind and it depends on the company you're applying to. I'll cover it all, enjoy!

Curriculum for this course:
https://github.com/llSourcell/Machine_Learning_Journey

Please Subscribe! And like. And comment. That's what keeps me going.

Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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instagram: https://www.instagram.com/sirajraval

More learning resources:
lhttps://www.linkedin.com/in/sirajraval/
https://www.theschool.ai/pages/jobs?p=1
https://novoresume.com/
https://www.wordclouds.com/
https://www.topresume.com/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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https://www.theschool.ai

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4.2 Social Media for Developers

http://www.youtube.com/watch?v=PulyGf6trOk

Social Media can play a big role in every developers life. In this video, I’ll show you how you can use different social media platforms to help you learn cutting tech edge technologies, network, and promote your own brand. I'll talk about the role GitHub, Quora, Youtube, Linkedin, Facebook, and other mainstream platforms play in helping you build a career in this field.

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Twitter: https://twitter.com/sirajraval
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instagram: https://www.instagram.com/sirajraval

This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More learning resources:
https://medium.com/machine-learning-in-practice/my-curated-list-of-ai-and-machine-learning-resources-from-around-the-web-9a97823b8524
https://news.ycombinator.com/
https://www.quora.com/
https://medium.com/@lahorekid/a-list-of-the-best-subreddits-for-data-science-machine-learning-and-data-visualization-84d76b83831e

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https://www.theschool.ai

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4.3 How to Use GitHub

http://www.youtube.com/watch?v=Loav1kbA640

GitHub is the most popular platform for developers across the world to share and collaborate on programming projects together. In this video, i'll explain how the git protocol works using the analogy of a car company (Tesla), how github works, and then show you from command line how you can push your first repository to github on your own computer.

Git commands in this video:
https://github.com/llSourcell/How-to-Use-GitHub

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More learning resources:
https://guides.github.com/activities/hello-world/
https://product.hubspot.com/blog/git-and-github-tutorial-for-beginners
https://try.github.io/
http://kbroman.org/github_tutorial/
https://blog.udacity.com/2015/06/a-beginners-git-github-tutorial.html

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http://wizards.herokuapp.com/

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4.4 100 Days of ML Code Challenge

http://www.youtube.com/watch?v=cuQMBj1cWPo

Who’s ready to take the 100 days of ML code challenge? That means coding and/or studying machine learning for at least an hour everyday for the next 100 days. Pledge with the #100DaysOfMLCode hashtag on your social media platform of choice. I’ll give the first few winners a shoutout!

Instructions here:
https://github.com/llSourcell/100_Days_of_ML_Code

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There are only 3 rules.
1 Make a public pledge using the #100DaysofMLCode hashtag on your favorite social media platform.
2 Make a public log of your work that you update daily, you can do this via GitHub a blog or a vlog.
3. if you see someone make a post using the #100DaysofMLCode hashtag, give it a like share or comment.

Project Idea:
https://www.drivendata.org/competitions/44/dengai-predicting-disease-spread/

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

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4.5 Backpropagation Explained

http://www.youtube.com/watch?v=FaHHWdsIYQg

The most popular optimization strategy in machine learning is called gradient descent. When gradient descent is applied to neural networks, its called back-propagation. In this video, i'll use analogies, animations, equations, and code to give you an in-depth understanding of this technique. Once you feel comfortable with back-propagation, everything else becomes easier. It uses calculus to help us update our machine learning models. Enjoy!

Code for this video:
https://github.com/llSourcell/backpropagation_explained

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More learning resources:
https://www.youtube.com/watch?v=XdM6ER7zTLk
https://www.youtube.com/watch?v=nhqo0u1a6fw
https://www.youtube.com/watch?v=jc2IthslyzM
https://www.youtube.com/watch?v=IHZwWFHWa-w
https://www.youtube.com/watch?v=umAeJ7LMCfU
http://neuralnetworksanddeeplearning.com/chap2.html

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4.6 Research to Code - Machine Learning tutorial

http://www.youtube.com/watch?v=pQyzdwHBbqo

A lot of times, research papers don't have an associated codebase that you can browse and run yourself. In cases like that, you'll have to code up the paper yourself. That is easier said than done, and in this video i'll show you how you should read and dissect a research paper so you can quickly implement it programmatically. The paper we'll be implementing in this video is called Neural Style transfer, that applies artistic filters to an image using 3 loss functions. Its a great starting point, i'll demo it using code, animations, and math. Enjoy!

Code for this video:
https://github.com/llSourcell/Research_to_Code


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instagram: https://www.instagram.com/sirajraval
Linkedin: https://www.linkedin.com/in/sirajraval/

github + code website is:
http://www.gitxiv.com/

More learning resources;
https://www.youtube.com/watch?v=-mu3TYZ_udM&t=2s
https://www.youtube.com/watch?v=SHTOI0KtZnU
https://medium.com/artists-and-machine-intelligence/neural-artistic-style-transfer-a-comprehensive-look-f54d8649c199
https://github.com/anishathalye/neural-style

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http://wizards.herokuapp.com/

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4.7 Financial Forecasting using Tensorflow.js (LIVE)

http://www.youtube.com/watch?v=5Uw1iSwvHH8

Can we use convolutional neural networks for time series analysis? It seems like a strange use case of convolutional networks, since they are generally used for image related tasks. But in recent months, more and more papers have started using convolutional networks for sequence classification. And since stock prices are a sequence, we can use them to make predictions. In this video, i'll use the popular tensorflow.js library to test out a prediction model for Apple stock. I'll also talk about how recurrent networks work as background. This is my first proper live stream in a year. Get hype!

Code for this video:
https://github.com/llSourcell/Financial_Forecasting_with_TensorflowJS

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Twitter: https://twitter.com/sirajraval
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instagram: https://www.instagram.com/sirajraval

This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More learning resources:
https://www.youtube.com/watch?v=V8DYi2G7nzg
https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/
https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877
https://medium.com/@TalPerry/deep-learning-the-stock-market-df853d139e02
https://www.youtube.com/watch?v=JuLCL3wCEAk

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http://wizards.herokuapp.com/

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https://www.theschool.ai

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4.8 Bitcoin Trading Bot (Tutorial)

http://www.youtube.com/watch?v=F2f98pNj99k

Cryptocurrency can be a high-risk, high-reward game for those willing to deal with the volatility. Can we use AI to help us make predictions about Bitcoin's future price? In this video, i'll show you how to build a simple Bitcoin trading bot using an LSTM neural network in Keras. Along the way I'll explain why we use LSTM networks through code and animations, as well as a review of the vanishing gradient problem.

Code for this video:
https://github.com/llSourcell/Bitcoin_Trading_Bot

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Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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instagram: https://www.instagram.com/sirajraval

This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources:
https://medium.com/swlh/developing-bitcoin-algorithmic-trading-strategies-bfdde5d5f6e0
https://bitcoin.stackexchange.com/questions/48093/how-to-build-a-bitcoin-trading-bot
https://blog.patricktriest.com/analyzing-cryptocurrencies-python/
https://github.com/lefnire/tforce_btc_trader

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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https://www.theschool.ai

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4.9 Reinforcement Learning for Stock Prediction

http://www.youtube.com/watch?v=05NqKJ0v7EE

Can we actually predict the price of Google stock based on a dataset of price history? I’ll answer that question by building a Python demo that uses an underutilized technique in financial market prediction, reinforcement learning. The specific technique we'll use in this video is a subset of RL called Q learning. Using a combination of code, animations, and theory i'll explain how we can let our AI learn a policy for when to buy and sell google stock to maximize profit.

Code for this video:
https://github.com/llSourcell/Reinforcement_Learning_for_Stock_Prediction

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Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources:
http://cs229.stanford.edu/proj2006/Molina-StockTradingWithRecurrentReinforcementLearning.pdf
http://www.wildml.com/2018/02/introduction-to-learning-to-trade-with-reinforcement-learning/
https://medium.com/@ranko.mosic/predicting-price-movement-and-trading-using-reinforcement-learning-kearns-nevmyvaka-2013-b5a64daa34f0
https://hub.packtpub.com/develop-stock-price-predictive-model-using-reinforcement-learning-tensorflow/
https://iknowfirst.com/deep-reinforcement-learning-part-2-the-game-of-stock-trading
https://www.youtube.com/watch?v=v_L9jR8P-54&list=PLQVvvaa0QuDe6ZBtkCNWNUbdaBo2vA4RO

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693
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4.10 Machine Learning API Tutorial (LIVE)

http://www.youtube.com/watch?v=YJyRBPz4CoM

Lets build a simple machine learning API together! I'll use the now classic neural style transfer algorithm to create a simple API that takes in an image and returns a stylized version of it. We'll use the FloydHub cloud service to both train and serve our model in the cloud. We can easily turn a deep neural network into a REST API that anyone can use, i'll detail those steps in this live stream and we'll build it using Tensorflow.

Code for this video:
https://github.com/llSourcell/Machine-Learning-API-Tutorial

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Twitter: https://twitter.com/sirajraval
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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources:
https://docs.floydhub.com/getstarted/quick_start/
https://harishnarayanan.org/writing/artistic-style-transfer/
https://medium.com/artists-and-machine-intelligence/neural-artistic-style-transfer-a-comprehensive-look-f54d8649c199
https://blog.paperspace.com/art-with-neural-networks/
https://rare-technologies.com/machine-learning-benchmarks-hardware-providers-gpu-part-2/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Sign up for the next course at The School of AI:
https://www.theschool.ai

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https://www.patreon.com/user?u=3191693
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4.11 Drake In My Feelings Challenge AI

http://www.youtube.com/watch?v=prswDGGmYaE

Kiki do you love me? The hip-hop artist Drake recently released a song called "in My Feelings' that has become a viral dance challenge. Everyone from Will Smith, to Ciara, to suburban dentists are showing off their dance moves to this popular song as part of the challenge. But rather than submit myself dancing, I've submitted an AI doing it for me. I'll detail how i did it in this video, concluding with a coding challenge.

Coding challenge:
https://github.com/llSourcell/InMyFeelings_Challenge

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Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
instagram: https://www.instagram.com/sirajraval
Facebook: https://www.facebook.com/sirajology

More Learning Resources:
https://www.youtube.com/watch?v=3WSgJCYIewM
https://www.youtube.com/watch?v=dcCDGuJ0c_4
https://www.youtube.com/watch?v=Sc7RiNgHHaE&t=204s
https://github.com/MontrealAI/posenet-v3
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
https://www.apple.com/final-cut-pro/trial/

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4.12 Loss Functions Explained

http://www.youtube.com/watch?v=IVVVjBSk9N0

Which loss function should you use to train your machine learning model? The huber loss? Cross entropy loss? How about mean squared error? If all of those seem confusing, this video will help. I'm going to explain the origin of the loss function concept from information theory, then explain how several popular loss functions for both regression and classification work. Using a combination of mathematical notation, animations, and code, we'll see how and when to use certain loss functions for certain types of problems.

Code for this video:
https://github.com/llSourcell/loss_functions_explained

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources:
http://www.informit.com/articles/article.aspx?p=2447200&seqNum=2
https://medium.com/data-science-group-iitr/loss-functions-and-optimization-algorithms-demystified-bb92daff331c
http://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html
https://blog.algorithmia.com/introduction-to-loss-functions/
http://yeephycho.github.io/2017/09/16/Loss-Functions-In-Deep-Learning/
https://stackoverflow.com/questions/42877989/what-is-a-loss-function-in-simple-words
http://rohanvarma.me/Loss-Functions/

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4.13 Kaggle Challenge (LIVE)

http://www.youtube.com/watch?v=suRd3UzdBeo

Join me as I attempt a Kaggle challenge live! In this stream, i'm going to be attempting the NYC Taxi Duration prediction challenge. I'll by using a combination of Pandas, Matplotlib, and XGBoost as python libraries to help me understand and analyze the taxi dataset that Kaggle provides. The goal will be to build a predictive model for taxi duration time. I'll also be using Google Colab as my jupyter notebook. Get hype!

Code for this video:
https://github.com/llSourcell/kaggle_challenge

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources:
https://www.kaggle.com/kanncaa1/machine-learning-tutorial-for-beginners
https://www.kaggle.com/rtatman/beginner-s-tutorial-python
https://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-learning/
http://blog.kaggle.com/2017/01/23/a-kaggle-master-explains-gradient-boosting/

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4.14 AWS Training Video

http://www.youtube.com/watch?v=zkzED9HvMG0

Amazon Web Service (AWS) offers developers a lot of services, from compute to data storage to serverless functions. In this video, we'll use AWS to train an AI to predict whether or not a customer will churn from using our service. Along the way, I'll explain how different components of the compute service like EC2, Elastic Beanstalk, LightSail, and the EC2 container service work. We'll also look at how SageMaker makes the whole pipeline much faster for beginners. The XGBoost technique will give us some favorable results, and I'll explain why at the end. Amazon did not pay me to make this video. Enjoy!

Code for this video:
https://github.com/llSourcell/Amazon_Training_Video

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources:
https://www.youtube.com/watch?v=mZ5H8sn_2ZI
https://www.youtube.com/watch?v=ubCNZRNjhyo
https://www.youtube.com/watch?v=N89AffsxS-g&t=2044s
https://aws.amazon.com/blogs/aws/sagemaker/
https://aws.amazon.com/sagemaker

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4.15 Serverless Computing with Google Cloud

http://www.youtube.com/watch?v=tdhVXKf_WSs

Serverless computing is a cloud-computing model where the cloud provider acts as the server, dynamically managing the allocation of machine resources. What that means is 'pay-as-you-use' pricing for the developer for their computing tasks. In this video i'll walk through some useful parts of the Google Cloud suite of services and use it to help train+test a wide+deep neural network to predict customer purchasing power. Topics covered include cloud computing models, kubernetes, Colab, and regression models. Enjoy!

Code for this video:
https://github.com/llSourcell/serverless_computing_with_google_Cloud

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources:
https://medium.com/google-cloud/gcp-the-google-cloud-platform-compute-stack-explained-c4ebdccd299b
https://cloud.google.com/products/ai/
https://cloud.google.com/ml-engine/docs/
https://www.youtube.com/watch?v=gVz9jKE_9iU
https://www.youtube.com/watch?v=COSXg5HKaO4
https://www.youtube.com/watch?v=0fsU_2wtzfM

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4.16 Node JS Machine Learning (LIVE)

http://www.youtube.com/watch?v=CMank9YmtTM

In this live stream, i'll build a real-time translation app from scratch using Node.JS and Tensorflow.js. We'll learn how machine learning can be used to help translate languages theoretically and programmatically. We'll also learn about how Node + Tensorflow work together and what the modern web development workflow that includes machine learning looks like. Get hype!

Code for this video:
https://github.com/llSourcell/Node_JS_Machine_Learning

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine...

More Learning Resources:
https://towardsdatascience.com/sequence-to-sequence-tutorial-4fde3ee798d8
https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html
https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html
https://machinelearningmastery.com/develop-encoder-decoder-model-sequence-sequence-prediction-keras/
https://dzone.com/articles/quick-introduction-how-nodejs
https://github.com/tensorflow/tfjs-examples

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4.17 Best Programming Languages for Machine Learning

http://www.youtube.com/watch?v=-cdxxrbKdho

What is the best programming language to learn for machine learning? There are a lot options, and a lot of opinions on this. I'm going to describe the top 3, using code, animations, and data to validate my point. We'll learn about tensorflow.js, several python libraries, and why C++ compiles code so fast. All of that in just 8 minutes, enjoy!

Code for this video:
https://github.com/llSourcell/Best-Programming-Languages-for-Machine-Learning

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine...

More Learning Resources:
https://www.youtube.com/watch?v=T5pRlIbr6gg&vl=en
https://learnpythonthehardway.org/
https://github.com/tensorflow/tfjs-examples
https://blog.bitsrc.io/11-javascript-machine-learning-libraries-to-use-in-your-app-c49772cca46c
https://www.youtube.com/watch?v=1cHx1baKqq0
http://www.cplusplus.com/doc/tutorial/

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4.18 Binary Logistic Regression Tutorial

http://www.youtube.com/watch?v=H6ii7NFdDeg

Binary logistic regression is a machine learning algorithm most useful when we want to model the event probability for a categorical response variable with two outcomes (yes/no, true/false, etc.). In this video we'll build a sentiment classifier app that uses binary logistic regression to classify tweets as either happy, sad, or neutral. I'll use animations, code, rap, skits, and equations to explain how it all works. Enjoy!

Code for this video:
https://github.com/llSourcell/logistic_regression

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine...

More Learning Resources:
https://github.com/awesomedata/awesome-public-datasets
http://www.statisticssolutions.com/what-is-logistic-regression/
https://codesachin.wordpress.com/2015/08/16/logistic-regression-for-dummies/
https://www.youtube.com/watch?v=zAULhNrnuL4
https://machinelearningmastery.com/logistic-regression-for-machine-learning/
https://towardsdatascience.com/the-logistic-regression-algorithm-75fe48e21cfa

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Original repo:
https://github.com/guillermo-carrasco/logistic-sentiment
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4.19 PyTorch Coding Challenge (LIVE)

http://www.youtube.com/watch?v=TvwYV0viIQE

PyTorch is a popular deep learning library released by Facebook's AI Research lab. In this video, I'll explain some of its unique features, then use it to solve the Kaggle "Invasive Species Monitoring Challenge". Sometimes, certain species of plants can slowly destroy an ecosystem if left unchecked. We're going to build a ResNet classifier using PyTorch to help detect which plants need to be removed to help the larger ecosystem survive. Get hype! Starts at 2:18

Code for this video:
https://github.com/llSourcell/Pytorch_Coding_Challenge_LIVE

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine...

More Learning Resources:
https://github.com/pytorch/examples
https://pytorch.org/
https://medium.com/@14prakash/almost-any-image-classification-problem-using-pytorch-i-am-in-love-with-pytorch-26c7aa979ec4
https://www.youtube.com/watch?v=FloMHMOU5Bs

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4.20 Neural Arithmetic Logic Units

http://www.youtube.com/watch?v=v9E7Wg0dHiU

Deepmind released a paper just a few days ago describing a module for neural networks called the Neural Arithmetic Logic Unit (NALU). Although deep neural networks can learn to represent and manipulate numerical information, they don't generalize well outside of the range of numbers encountered during training. Meaning train it on the numbers 1-10 and it won't be able to count to 11. To improve this ability, the researchers created an architecture that represents numerical quantities as linear activations which are manipulated using primitive arithmetic operators, controlled by learned gates. Its really fascinating stuff, i'll detail how it works in this video.

Code for this video:
https://github.com/llSourcell/Neural_Arithmetic_Logic_Units

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources:
https://arxiv.org/abs/1808.00508
https://github.com/search?l=Python&q=nalu&type=Repositories
https://deepmind.com/blog/
https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3

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4.21 OpenAI Five vs Dota 2 Explained

http://www.youtube.com/watch?v=DzzFSyzv1p0

How did OpenAI's team of 5 neural networks manage to beat some of the world's best DOTA 2 players? Also, why would OpenAI dedicate so much time and energy to defeating video game players? In this video, I'll explain in detail the cutting edge research techniques OpenAI used to create such an incredible AI algorithm, and how it could be used in the real world. These techniques include Long Short Term Memory Recurrent Neural Networks, Proximal Policy Optimization, and a custom rollout system they've dubbed 'Rapid'.

Code for this video:
https://github.com/llSourcell/OpenAI_Five_vs_Dota2_Explained

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources:
https://blog.openai.com/openai-five/
https://towardsdatascience.com/the-science-behind-openai-five-that-just-produced-one-of-the-greatest-breakthrough-in-the-history-b045bcdc2b69
https://blog.openai.com/openai-baselines-ppo/
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
https://www.youtube.com/watch?v=i_McNBDP9Qs&vl=en

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4.22 DeepMind AlphaGo Zero Explained

http://www.youtube.com/watch?v=UzYeqAJ2bA8

DeepMind's AlphaGo Zero algorithm beat the best Go player in the world by training entirely by self-play. It played against itself repeatedly, getting better over time with no human gameplay input. AlphaGo Zero was a remarkable moment in AI history, a moment that will always be remembered. Move 37 in particular is worthy of many philosophical debates. You'll see what I mean and get a technical overview of its neural components (code + animations) in this video. Enjoy!

Code for this video:
https://github.com/Zeta36/chess-alpha-zero

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There are 2 errors in this video:
1. At the top of the residual network, it says value layer twice. One should say 'policy' layer.
2 The residual network is 40 layers, i say 20.

This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources:
https://deepmind.com/blog/alphago-zero-learning-scratch/
https://medium.com/applied-data-science/alphago-zero-explained-in-one-diagram-365f5abf67e0
https://hackernoon.com/the-3-tricks-that-made-alphago-zero-work-f3d47b6686ef
https://web.stanford.edu/~surag/posts/alphazero.html
http://tim.hibal.org/blog/alpha-zero-how-and-why-it-works/
http://www.jessicayung.com/alphago-zero-an-overview-of-the-algorithm/

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4.23 Rachel Thomas - Really Quick Questions with a Fast.AI Researcher

http://www.youtube.com/watch?v=RsOpnFbufcY

In this interview, I ask Fast.AI researcher Rachel Thomas 67 questions about machine learning and her day to day life. She was selected by Forbes as one of “20 Incredible Women in AI”, was an early engineer at Uber, and earned her math PhD at Duke. She is co-founder of fast.ai, which created the “Practical Deep Learning for Coders” course that over 100,000 students have taken. Rachel is a popular writer and keynote speaker. Her writing has been read by over half a million people; has been translated into Chinese, Spanish, Korean, & Portuguese; and has made the front page of Hacker News 8x.

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More Learning resources:
http://www.fast.ai/
https://twitter.com/math_rachel
https://twitter.com/fastdotai
https://www.youtube.com/watch?v=NlqT_MTH-nw

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4.24 Azure Machine Learning

http://www.youtube.com/watch?v=LQEyK4POowk

Micosoft Azure has a drag and drop interface that lets you build, train, and test models pretty easily. In this video, I'll explain how Azure compares to other cloud offerings, the idea of the hybrid cloud, what its services are, and then we'll use its Machine Learning service to build an automobile price prediction model (linear regression). No one paid me to make this. Enjoy!

Code for this video:
https://github.com/llSourcell/azure_machine_learning

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources:
https://azure.microsoft.com/en-us/
https://www.youtube.com/watch?v=7pmn6luCwQ4
https://www.youtube.com/watch?v=KXkBZCe699A
https://www.youtube.com/watch?v=TjwRj1LrFSo
https://www.youtube.com/watch?v=csFDLUYnq4w&t=362s
https://www.expeditedssl.com/azure-in-plain-english

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#SirajRaval #MicrosoftAzure #HowTo
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4.25 How to Study Machine Learning

http://www.youtube.com/watch?v=waXHrc2m9K8

Let me show you the techniques I use to study machine learning in this video. That includes living a healthy lifestyles, optimizing your learning environment, creating a personalized learning path, prioritizing effectively, and being an active learner. I'll demo the FAST technique, which you can use to help learn faster and more efficiently. I made this with machine learning technology in mind, but these techniques can be used for any field. Enjoy!

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More Learning Resources:
http://jimkwik.com/kwik-brain-001/
https://github.com/llSourcell/Learn_Deep_Learning_in_6_Weeks
https://hbr.org/2018/07/take-control-of-your-learning-at-work
https://www.youtube.com/watch?v=nxWfZP6eslM
https://www.youtube.com/watch?v=YzfdL58virc
https://www.youtube.com/watch?v=cuQMBj1cWPo&t=7s

Here is a v1 list of School of AI Chapters (cleaner list coming ASAP):
https://www.google.com/maps/d/u/0/viewer?mid=1fmlKwZZXGoNvZnbZlnPazvTcPcJ89nno&ll=27.620243622926218%2C40.21712610000009&z=2

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4.26 Quantum Machine Learning

http://www.youtube.com/watch?v=DmzWsvb-Un4

Quantum computers are mind bogglingly powerful machines that take a novel approach to processing data. Built on the principles of quantum mechanics, they utilize complex and fascinating laws of nature that are always there, but usually remain hidden from view like superposition and entanglement. In this video, i'll talk about the intersection of quantum computing and machine learning. Specifically, we'll discuss the examples of quantum annealing, sampling, and quantum gates as layers in a neural network. We'll first try to cover quantum mechanics though, get hype!

Code for this video:
https://github.com/llSourcell/quantum_machine_learning

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More learning resources:
https://github.com/krishnakumarsekar/awesome-quantum-machine-learning
https://hackernoon.com/how-quantum-computing-machine-learning-work-together-bc61d0f1b3a
https://www.kdnuggets.com/2018/01/quantum-machine-learning-overview.html
https://medium.com/xanaduai/quantum-machine-learning-1-0-76a525c8cf69
https://www.rolandberger.com/en/Point-of-View/The-next-big-thing-Quantum-machine-learning.html
https://www.rigetti.com/products

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https://www.theschool.ai

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#SirajRaval #Quantum #MachineLearning
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4.27 C Programming for Machine Learning (LIVE)

http://www.youtube.com/watch?v=giF8XoPTMFg

The ability to write implementations of machine learning algorithms in pure C allows developers to very efficiently manage memory allocation, concurrency, and control flow. That means fast implementations that can outperform preexisting models in other languages, including even (gasp) Python. It’s a useful skill to know and in this live stream I’ll use C and C-based Python tools like Cython + spaCy to develop some really fast natural language processing algorithms for text data. We’ll be able to tokenize, tag, normalize, vectorize, and dependency parse articles of text to derive valuable insights. No installation necessary, we'll do this together using Google Colab in the browser. Join me, there’s a lot to cover here!

Code for this video:
https://github.com/llSourcell/c_programming_for_machine_learning

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This video is apart of my Machine Learning Journey course:
https://github.com/llSourcell/Machine_Learning_Journey

More learning resources:
https://pydata.org/berlin2016/schedule/presentation/51/
https://smerity.com/articles/2018/cython_for_high_and_low.html
https://explosion.ai/blog/writing-c-in-cython
https://spacy.io/api/cython
https://medium.com/huggingface/100-times-faster-natural-language-processing-in-python-ee32033bdced

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Learn more about the School of AI:
https://www.theschool.ai

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https://www.patreon.com/user?u=3191693
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4.28 Robotic Manipulation Explained

http://www.youtube.com/watch?v=mCI-f71MAvY

Robotics is a vast field of study, encompassing theories across multiple scientific disciplines. In this video, we'll program a robotic arm in a simulated environment to pick up an object. Along the way, we'll learn about both forward and inverse kinematics. We'll optimize our arms trajectory using calculus and observe how its angles change over time, measuring them with trigonometry. We'll code this in Python, this is an example of machine learning applied to robotic manipulation. Enjoy!

Code for this video:
https://github.com/llSourcell/Robotic_Manipulation

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More educational links here:
https://blog.robotiq.com/how-to-calculate-a-robots-forward-kinematics-in-5-easy-steps
http://courses.csail.mit.edu/6.141/spring2011/pub/lectures/Lec14-Manipulation-II.pdf
https://www.alanzucconi.com/2017/04/06/forward-kinematics/
https://appliedgo.net/roboticarm/
http://www.ent.mrt.ac.lk/~rohan/teaching/ME5144/LectureNotes/Lec%205%20Kinematics.pdf

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School of AI
https://www.theschool.ai

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5 Learning Strategies

5.1 How to Learn Mathematics Fast

http://www.youtube.com/watch?v=YzfdL58virc

Whether you're interested in AI or you just want to do some real engineering work, you’re going to need to brush up on your math skills. In this video, I’ll describe my strategy to learn mathematics as fast as possible. Math is a specific, powerful vocabulary for ideas and giving a structure to the way you learn it will empower you to absorb much more of it much faster. I'll go over my strategies in order.

Math resources:
https://github.com/llSourcell/learn_math_fast

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More learning resources:
http://www.souravsengupta.com/cds2016/lectures/Savov_Notes.pdf
http://tutorial.math.lamar.edu/pdf/Calculus_Cheat_Sheet_All.pdf
http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf
https://static1.squarespace.com/static/54bf3241e4b0f0d81bf7ff36/t/55e9494fe4b011aed10e48e5/1441352015658/probability_cheatsheet.pdf
https://brilliant.org/
https://triseum.com/variant-limits/

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5.2 How to Do Freelance AI Programming

http://www.youtube.com/watch?v=x6GYD5WPda4

You can build a sustainable full-time income from doing freelance AI programming work. In this video, i'm going to show you the steps you can take to start your journey as a freelancer. Whether you're a student or are employed full-time, you can begin the process of planning out a freelance career today. Getting clients, leveling up your skills, marketing yourself, setting up your financials, tools to help optimize your workflow, these are all aspects of the freelance life that i'll explain from my own personal experience.

List of resources:
https://github.com/llSourcell/AI_Freelancing

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Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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Job marketplaces:
http://upwork.com/
https://www.freelancer.com/
https://www.toptal.com/
https://angel.co/
http://founderdating.com/
https://www.guru.com/
https://weworkremotely.com/

Networking:
https://www.kdnuggets.com/meetings/
https://conferences.oreilly.com/strata
https://www.datasciencecentral.com/

Tools:
https://www.codementor.io/freelance-rates
https://www.bunq.com/business
https://www.hellobonsai.com/

Learning resources:
https://github.com/Mybridge/machine-learning-open-source/blob/master/src/05-2018.md
https://www.youtube.com/playlist?list=PL2-dafEMk2A6oABirZ1Ug805Ag-8W54rN
https://www.youtube.com/playlist?list=PL2-dafEMk2A5_Fcpl3FHOjo2Gfios3b5o

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http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693
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5.3 How to Read Math Equations

http://www.youtube.com/watch?v=-mu3TYZ_udM

Mathematics is its own language, and not enough people speak this language. I'm going to show you some key steps necessary for you to be able to read any math equation. Memorization techniques, grammar, structure, rules, it all comes together to help you form an intuition around the language of the Universe. Machine Learning, cryptography, robotics, all of the cool topics in Computer Science use math heavily so its best to master it when you can. I'll go over 2 my thought process of analyzing 2 equations for some papers to give you some insight into how I think about these things.

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Deep Learning cheatsheet:
https://hackernoon.com/deep-learning-cheat-sheet-25421411e460

Math of Computer Science at MIT:
https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/

Math of Intelligence:
https://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D

More learning resources:
https://www.youtube.com/watch?v=Syj8FVr7vbM
https://www.youtube.com/watch?v=ze3bDrg1tJ8
https://www.youtube.com/watch?v=8i9-9zHbW6g
https://www.youtube.com/watch?v=l3XzepN03KQ

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5.4 How to Write a Research Paper

http://www.youtube.com/watch?v=S47RIVkr978

I'm going to go over the steps you can take to write your first research paper! Research papers have long been something only academics did, but the Internet has offered us several ways to democratize this process. Journals like Arxiv are open for public submissions, machine learning papers are generally open source so anyone can learn from them, and online communities offer advice in the way previously only a professor could. I'll go through these tips in order in as much detail as I can on how to write a research paper.

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Twitter: https://twitter.com/sirajraval
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More learning resources:
https://www.youtube.com/watch?v=1AYxMbYZQ1Y
https://www.youtube.com/watch?v=UiTaxAfIBPg
https://www.youtube.com/watch?v=oPobmEZ4lfs&t=242s
https://www.youtube.com/watch?v=KlgR1q3UQZE
https://www.youtube.com/watch?v=DS2DOEkorDo&t=220s

OpenAI's request for research:
https://openai.com/requests-for-research/


Some of my papers:
http://www.sirajcoin.io/whitepaper.html
https://docs.google.com/document/d/1QFyBUV8pKqgl__4J1zT0BmIYfTYF8hnlyalOo7PJvLM/edit?usp=sharing (i turned this one into a book actually [Decentralized Applications])

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http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693
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https://goo.gl/FZzJ5w

5.5 How to Read a Research Paper

http://www.youtube.com/watch?v=SHTOI0KtZnU

Ever wondered how I consume research so fast? I'm going to describe the process i use to read lots of machine learning research papers fast and efficiently. It's basically a 3-pass approach, i'll go over the details and show you the extra resources I use to learn these advanced topics. You don't have to be a PhD, anyone can read research papers. It just takes practice and patience.

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More learning resources:
http://www.arxiv-sanity.com/
https://www.reddit.com/r/MachineLearning/
https://www.elsevier.com/connect/infographic-how-to-read-a-scientific-paper
https://www.quora.com/How-do-I-start-reading-research-papers-on-Machine-Learning
https://www.reddit.com/r/MachineLearning/comments/6rj9r4/d_how_do_you_read_mathheavy_machine_learning/
https://machinelearningmastery.com/how-to-research-a-machine-learning-algorithm/
http://www.sciencemag.org/careers/2016/03/how-seriously-read-scientific-paper

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5.6 How to Learn Advanced Concepts Fast

http://www.youtube.com/watch?v=nxWfZP6eslM

These are 10 strategies I use to learn advanced concepts as fast as possible! I'm going to explain each one, and give some examples of what I mean. No coding challenge this week, hope its helpful!

10 Strategies below:

1. Find a reason to learn
2. Start with the simplest explanations
3. Create a set of small, achievable goals
4. Set Deadlines
5. Maintain a flow state
6. Let your curiosity guide your learning path
7. Spend 1/3 of your time researching & 2/3 doing
8. Take notes by hand
9. Dont multitask
10. Maintain your Health

(yes, i mis-numbered some of them in the video accidentally)

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5.7 How to Get an AI Internship

http://www.youtube.com/watch?v=CGTn0ceOaOM

This is a question I get asked a lot, so I've decided to make a video detailing how to get an AI internship. Internships are a great way to start a career in AI! They enable you to build a professional network and can be amazing learning experiences. I'll list a ton of resources and discuss the most helpful steps in the process including creating a study plan, finding a relevant position, building a personal brand, leveraging your existing network, and practicing for interviews. Enjoy!

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Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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instagram: https://www.instagram.com/sirajraval

The School of AI:
https://www.theschool.ai

I wasn't kidding about the million dollar salaries bit:
https://www.nytimes.com/2018/04/19/technology/artificial-intelligence-salaries-openai.html

Who to Follow in AI:
https://medium.com/@alexrachnog/ultimate-following-list-to-keep-updated-in-artificial-intelligence-32776ffcd079

Learn Machine Learning in 3 Months:
https://www.youtube.com/watch?v=Cr6VqTRO1v0

How to Learn Math Fast:
https://www.youtube.com/watch?v=YzfdL58virc&vl=en

Job listings:
https://intern.supply/
https://www.angel.co

Project Ideas:
https://github.com/NirantK/awesome-project-ideas
https://github.com/llSourcell

How to Read Research Papers:
https://www.youtube.com/watch?v=SHTOI0KtZnU&t=42s

How to Write Research Papers:
https://www.youtube.com/watch?v=S47RIVkr978

How to Create a Great AI Resume:
https://www.youtube.com/watch?v=nMK94JlKRb4

How to Succeed in any Programming Interview:
https://www.youtube.com/watch?v=5KB5KAak6tM&t=102s

Join us in the Wizards Slack channel (join #internships):
http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693

#SirajRaval

Signup for my newsletter for exciting updates in the field of AI:
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Hiring? Need a Job? See our job board!:
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www.theschool.ai/consulting-group/

6 AI for Business

6.1 AI in Medicine | Medical Imaging Classification (TensorFlow Tutorial)

http://www.youtube.com/watch?v=DCcmFXXAHf4

Can AI be used to detect various diseases from a simple body scan? Yes! Normally, doctors train for years to do this and the error rate is still relatively high. From mammograms to cat scans, AI can diagnose a disease better than any human can if given the right training dataset. This will drastically reduce patient death, save medical practices a lot of money, and aid doctors in the patient care process. Everyone will win and its important to remember that AI won't replace doctors, it will become the most powerful tool they've ever used. And once enough AI startups start impacting the field of healthcare, it will become as common a tool as the stethoscope has been.

Code for this video:
https://github.com/llSourcell/AI_in_Medicine_Clinical_Imaging_Classification

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Curriculum:
https://github.com/llSourcell/AI_For_Business_Curriculum

More learning resources:
https://www.youtube.com/watch?v=3LkbUxqGTfo
https://www.youtube.com/watch?v=S4GvBCMfRew
https://www.youtube.com/watch?v=LxHHsujnF9c
https://www.youtube.com/watch?v=ZPXCF5e1_HI
https://www.youtube.com/watch?v=QfNvhPx5Px8&t=202s

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Sign up for the next course at The School of AI:
https://www.theschool.ai

https://github.com/gregwchase/dsi-capstone

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https://www.patreon.com/user?u=3191693
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6.2 AI in Medicine | Drug Discovery with GANs (TensorFlow Tutorial)

http://www.youtube.com/watch?v=hY9Bc3mtphs

How do we use AI to cure drug discovery? This is apart of my AI for business series right here on Youtube. Subscribe to stay up to date! In this video I'm going to cover how the drug discovery process works in clinical labs and how AI can be used to speed up that process by orders of magnitude. We'll look at 3 different papers that used different types of neural networks, and the last one is what we'll focus on; the General Adversarial Network.

Code for this video:
https://github.com/llSourcell/AI_for_healthcare

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Curriculum:
https://github.com/llSourcell/AI_For_Business_Curriculum

More learning resources:
https://github.com/plotly/dash-drug-discovery-demo
https://www.youtube.com/watch?v=FTr3n7uBIuE&t=25s
https://www.youtube.com/watch?v=yz6dNf7X7SA
https://www.youtube.com/watch?v=Sw9r8CL98N0
http://www.healthcareitnews.com/slideshow/how-ai-transforming-healthcare-and-solving-problems-2017

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Sign up for the next course at The School of AI:
https://www.theschool.ai

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https://www.patreon.com/user?u=3191693
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6.3 Stock Price Prediction | AI in Finance

http://www.youtube.com/watch?v=7vunJlqLZok

Can AI be used in the financial sector? Of course! In fact, finance was one of the pioneering industries that started using AI in the early 80s for market prediction. Since then, major financial firms and hedge funds have adopted AI technologies for everything from portfolio optimization, to credit lending, to stock betting. In this video, we'll go over all the different ways AI can be used in applied finance, then build a stock price prediction algorithm in python using Keras and Tensorflow.

Code for this video:
https://github.com/llSourcell/AI_in_Finance

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Twitter: https://twitter.com/sirajraval
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More learning resources:
https://hackernoon.com/unsupervised-machine-learning-for-fun-profit-with-basket-clusters-17a1161e7aa1
https://www.datacamp.com/community/tutorials/finance-python-trading
http://www.cuelogic.com/blog/python-in-finance-analytics-artificial-intelligence/
https://www.udacity.com/course/machine-learning-for-trading--ud501
https://www.oreilly.com/learning/algorithmic-trading-in-less-than-100-lines-of-python-code

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Sign up for the next course at The School of AI:
https://www.theschool.ai

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https://www.patreon.com/user?u=3191693
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6.4 Fraud Prevention | AI in Finance

http://www.youtube.com/watch?v=UNgdIkuVC6g

Can AI be used for fraud prevention? Yes! In this video, we'll go over the history of fraud prevention techniques, then talk about some recent AI startups that are helping business reduce credit card fraud. We'll break down what the different AI models that help with fraud prevention look like (decision trees, logistic regression, neural networks) and finally, we'll try it out on a transaction dataset.

Code for this video:
https://github.com/llSourcell/AI_for_Financial_Data

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Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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instagram: https://www.instagram.com/sirajraval

More learning resources:
https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877
https://www.youtube.com/watch?v=GlV_QO5B2eU
https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data
https://pythonprogramming.net/python-programming-finance-machine-learning-framework/
https://gist.github.com/yhilpisch/648565d3d5d70663b7dc418db1b81676
https://www.quantopian.com/posts/simple-machine-learning-example

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Sign up for the next course at The School of AI:
https://www.theschool.ai

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Signup for my newsletter for exciting updates in the field of AI:
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6.5 AI in Marketing

http://www.youtube.com/watch?v=FYMjXD3G__Y

Audience targeting and automatic content creation are just a few of the many ways AI can be used to help grow your user base and increase sales. In this video, i'll go over some startups that are applying AI to the marketing space and then programmatically walk through some AI techniques like matrix factorization, SVD, and LSTM neural networks that help a marketer outperform the competition and get the optimal results for their business. We've got quite a lot to cover in this video!

Code for this video:
https://github.com/llSourcell/AI_In_Marketing

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Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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instagram: https://www.instagram.com/sirajraval

More learning resources:
https://www.youtube.com/watch?v=cdLUzrjnlr4
https://www.youtube.com/watch?v=BwmddtPFWtA
https://www.thinkwithgoogle.com/marketing-resources/ai-personalized-marketing/
https://medium.com/the-mission/how-to-boost-your-marketing-with-artificial-intelligence-8c092d7e3f7d
https://www.youtube.com/watch?v=9gBC9R-msAk

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Sign up for the next course at The School of AI:
https://www.theschool.ai

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w

6.6 Chatbot Tutorial | AI in Marketing

http://www.youtube.com/watch?v=PXJtFc8DjsE

Can we build a chatbot that acts as a conversational agent for a company brand? Yes! In this video we'll go over different techniques that let you build your own chatbot using AI technology. Specifically, we'll be discussing generative models and deep neural networks. There are a bunch of services we can use to build a chatbot using no code, but thats no fun right? I'll talk about a few of them as well as some startups that are doing amazing things in this marketing space using chatbots as a tool to engage their customers over messaging apps instead of social media. Tensorflow, python, and motivation are all we need!

Code for this video:
https://github.com/llSourcell/chatbot_tutorial

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Twitter: https://twitter.com/sirajraval
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More learning resources:
https://aws.amazon.com/lex/?sc_channel=PS&sc_campaign=lex_2017&sc_publisher=google&sc_medium=awns_lex_nb&sc_content=chatbot_p&sc_detail=chatbot&sc_category=lex&sc_segment=209069302261&sc_matchtype=p&sc_country=US&s_kwcid=AL!4422!3!209069302261!p!!g!!chatbot&ef_id=Wt@YdwAABBtmpBYI:20180506204248:s
https://apps.worldwritable.com/tutorials/chatbot/
https://chatbotsmagazine.com/tutorials/home
https://chatbotsmagazine.com/how-to-develop-a-chatbot-from-scratch-62bed1adab8c
https://chatbottutorial.com/
https://www.ibm.com/watson/how-to-build-a-chatbot/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Sign up for the next course at The School of AI:
https://www.theschool.ai

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6.7 AI for Scheduling

http://www.youtube.com/watch?v=nvLJq4GnCg4

AI allows for apps that can handle the complexity of scheduling a meeting between one person or a group of people. This is useful for recruiters and all sorts of teams. Natural Language processing, a subset of AI, focuses on learning from linguistic constructs and deriving meaning from it in a structured way. We'll build a slack bot that uses NLP to read the intent of the user and schedule a meeting accordingly. I talk about the architecture, code, and need for this software in this video. I also give some tips on how I use github near the end.

Code for this video:
https://github.com/llSourcell/AI_for_Scheduling

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More learning resources:
https://x.ai/
https://www.stottlerhenke.com/products/aurora/
https://www.myally.ai/
https://www.youtube.com/watch?v=XzkgjtP9lFQ
https://blog.init.ai/tutorial-building-a-conversational-booking-bot-with-init-ai-and-acuity-scheduling-e717df35adf6

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6.8 AI for Resumes

http://www.youtube.com/watch?v=p3SKx5C04qg

Recruiting is a 200 billion dollar industry thats all about judging potential job candidates and seeing if they're a good fit for a position at a company. Recruiters receive thousands of resumes and are responsible for analyzing all of them. Theres essentially a massive amount of data that these humans have to parse through and find the best ones. This is easily a problem machine learning can solve, we'll build an app that can classify resumes into 27 different job categories using natural language processing via a convolutional neural network. I'll explain how in this video. Also its midterm time, so see the link below for the midterm assignment.

Code for this video (with midterm):
https://github.com/llSourcell/AI_for_Resumes

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More learning resources:
https://medium.com/the-mission/how-i-turned-my-resume-into-a-bot-and-how-you-can-too-f03847352baa
https://www.textkernel.com/challenges-behind-parsing-matching-cvs-jobs/
https://www.quora.com/How-do-I-develop-a-resume-parser-using-NLP-Natural-Language-Processing?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa
https://dzone.com/articles/cv-r-cvs-retrieval-system-based-on-job-description
https://www.slideshare.net/zainulsayed39/218-intelligent

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6.9 AI for Supply Chain

http://www.youtube.com/watch?v=vwor9Fva1V4

Every product in your home is there as a result of being distributed across whats called a supply chain. The path that a commodity takes through manufacturing, transport, distribution centers, etc. is called the supply chain. The supply chain for most companies is riddled with inefficiencies. Late drivers, bad weather conditions, suboptimal planning, timing issues, the list of things that can go wrong is endless and most of this coordination is done by humans. AI can be used to optimize this entire pipeline, from planning to autonomous transport. In this video i'll demo an app using IBM's Logistic Wizard to optimize a simulated companies supply chain, as well as build a time series forecasting model using Keras to predict the price of a shipment on a certain date.

Code for this video:
https://github.com/llSourcell/AI_Supply_Chain

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More learning resources:
https://www.tandfonline.com/doi/abs/10.1080/13675560902736537
https://news.crunchbase.com/news/alloy-raises-12-million-to-bring-ai-to-supply-chain-management/
https://www.americanexpress.com/us/content/foreign-exchange/articles/using-AI-in-supply-chain-management/
https://www.forbes.com/sites/oracle/2018/03/07/data-the-new-ai-supply-chain/#2499f7b16049
https://www.ibm.com/us-en/marketplace/supply-chain-insights?S_PKG=OV60982&cm_mmc=Search_Google-_-IBM+Watson+Customer+Engagement_Watson+Supply+Chain+-+Supply+Chain+Insights-_-WW_NA-_-ai+in+supply+chain_Exact_OV60982&cm_mmca1=000020LZ&cm_mmca2=10006656&cm_mmca7=9061263&cm_mmca8=aud-295225167539:kwd-427960545262&cm_mmca9=9f6dfb3c-4b94-4f53-a67e-219704bef887&cm_mmca10=265871013815&cm_mmca11=e&mkwid=9f6dfb3c-4b94-4f53-a67e-219704bef887|1467|21744&cvosrc=ppc.google.ai%20in%20supply%20chain&cvo_campaign=000020LZ&cvo_crid=265871013815&Matchtype=e
https://medium.com/@KodiakRating/6-applications-of-artificial-intelligence-for-your-supply-chain-b82e1e7400c8

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6.10 Self Driving Cars Explained

http://www.youtube.com/watch?v=yt015gM-ync

Self driving cars are the future of transportation and will make up a crucial part of society as more drive related jobs are automated. In this video, i'll explain how the entire self driving car pipeline works, including computer vision, path planning, control, sensor fusion, and localization. We'll use the Udacity simulator to train our own self driving car with the Keras deep learning library as a tool at the end. This technology is surprisingly simple to understand, it just requires research into a couple of subfields, all of which i'll cover.

Code for this video:
https://github.com/llSourcell/self_driving_cars_explained

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https://www.ucsusa.org/clean-vehicles/how-self-driving-cars-work#.WwGlx9MvwmI
https://medium.com/swlh/everything-about-self-driving-cars-explained-for-non-engineers-f73997dcb60c
https://hackernoon.com/self-driving-cars-explained-db9fc8ced7e8
https://searchenterpriseai.techtarget.com/definition/driverless-car
https://www.youtube.com/watch?v=xMH8dk9b3yA
https://www.youtube.com/channel/UCq0imsn84ShAe9PBOFnoIrg
https://www.youtube.com/watch?v=FTr3n7uBIuE&t=1782s

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6.11 AI for Music Composition

http://www.youtube.com/watch?v=NS2eqVsnJKo

Machine learning algorithms make predictions based on a dataset. If that dataset is a collection of musical notes, the prediction would be a new collection of musical notes. We can consider that prediction the AI's unique composition. The question is, can an AI really compose music as well as humans can? In this video i'll go over some really popular models that have been used to generate music, from hidden markov models, to recurrent networks (with their variations), to the modern generative adversarial network. Code, theory, and demos included in this video. Enjoy!

Code for this video:
https://github.com/llSourcell/AI_For_Music_Composition

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More learning resources:
https://medium.com/artists-and-machine-intelligence/neural-nets-for-generating-music-f46dffac21c0
http://www.asimovinstitute.org/analyzing-deep-learning-tools-music/
https://magenta.tensorflow.org/
https://www.ampermusic.com/
https://blogs.technet.microsoft.com/machinelearning/2017/12/06/music-generation-with-azure-machine-learning/
https://salu133445.github.io/musegan/

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7 AI in Finance

7.1 Stock Price Prediction | AI in Finance

http://www.youtube.com/watch?v=7vunJlqLZok

Can AI be used in the financial sector? Of course! In fact, finance was one of the pioneering industries that started using AI in the early 80s for market prediction. Since then, major financial firms and hedge funds have adopted AI technologies for everything from portfolio optimization, to credit lending, to stock betting. In this video, we'll go over all the different ways AI can be used in applied finance, then build a stock price prediction algorithm in python using Keras and Tensorflow.

Code for this video:
https://github.com/llSourcell/AI_in_Finance

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More learning resources:
https://hackernoon.com/unsupervised-machine-learning-for-fun-profit-with-basket-clusters-17a1161e7aa1
https://www.datacamp.com/community/tutorials/finance-python-trading
http://www.cuelogic.com/blog/python-in-finance-analytics-artificial-intelligence/
https://www.udacity.com/course/machine-learning-for-trading--ud501
https://www.oreilly.com/learning/algorithmic-trading-in-less-than-100-lines-of-python-code

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7.2 Fraud Prevention | AI in Finance

http://www.youtube.com/watch?v=UNgdIkuVC6g

Can AI be used for fraud prevention? Yes! In this video, we'll go over the history of fraud prevention techniques, then talk about some recent AI startups that are helping business reduce credit card fraud. We'll break down what the different AI models that help with fraud prevention look like (decision trees, logistic regression, neural networks) and finally, we'll try it out on a transaction dataset.

Code for this video:
https://github.com/llSourcell/AI_for_Financial_Data

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More learning resources:
https://medium.com/mlreview/a-simple-deep-learning-model-for-stock-price-prediction-using-tensorflow-30505541d877
https://www.youtube.com/watch?v=GlV_QO5B2eU
https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data
https://pythonprogramming.net/python-programming-finance-machine-learning-framework/
https://gist.github.com/yhilpisch/648565d3d5d70663b7dc418db1b81676
https://www.quantopian.com/posts/simple-machine-learning-example

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8 AI in Medicine

8.1 AI in Medicine | Medical Imaging Classification (TensorFlow Tutorial)

http://www.youtube.com/watch?v=DCcmFXXAHf4

Can AI be used to detect various diseases from a simple body scan? Yes! Normally, doctors train for years to do this and the error rate is still relatively high. From mammograms to cat scans, AI can diagnose a disease better than any human can if given the right training dataset. This will drastically reduce patient death, save medical practices a lot of money, and aid doctors in the patient care process. Everyone will win and its important to remember that AI won't replace doctors, it will become the most powerful tool they've ever used. And once enough AI startups start impacting the field of healthcare, it will become as common a tool as the stethoscope has been.

Code for this video:
https://github.com/llSourcell/AI_in_Medicine_Clinical_Imaging_Classification

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Curriculum:
https://github.com/llSourcell/AI_For_Business_Curriculum

More learning resources:
https://www.youtube.com/watch?v=3LkbUxqGTfo
https://www.youtube.com/watch?v=S4GvBCMfRew
https://www.youtube.com/watch?v=LxHHsujnF9c
https://www.youtube.com/watch?v=ZPXCF5e1_HI
https://www.youtube.com/watch?v=QfNvhPx5Px8&t=202s

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https://github.com/gregwchase/dsi-capstone

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8.2 AI in Medicine | Drug Discovery with GANs (TensorFlow Tutorial)

http://www.youtube.com/watch?v=hY9Bc3mtphs

How do we use AI to cure drug discovery? This is apart of my AI for business series right here on Youtube. Subscribe to stay up to date! In this video I'm going to cover how the drug discovery process works in clinical labs and how AI can be used to speed up that process by orders of magnitude. We'll look at 3 different papers that used different types of neural networks, and the last one is what we'll focus on; the General Adversarial Network.

Code for this video:
https://github.com/llSourcell/AI_for_healthcare

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Curriculum:
https://github.com/llSourcell/AI_For_Business_Curriculum

More learning resources:
https://github.com/plotly/dash-drug-discovery-demo
https://www.youtube.com/watch?v=FTr3n7uBIuE&t=25s
https://www.youtube.com/watch?v=yz6dNf7X7SA
https://www.youtube.com/watch?v=Sw9r8CL98N0
http://www.healthcareitnews.com/slideshow/how-ai-transforming-healthcare-and-solving-problems-2017

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8.3 Biotechnology

http://www.youtube.com/watch?v=7SBt6mMNCHk

A Chinese scientist recently claimed to have created the first genetically modified human embryos. Using a technology called CRISPR, he made twin baby girls resistant to HIV before they were born. This opened up serious debate across the world on the implications of this technology. I'm going to take this opportunity to give an in-depth analysis of biotechnology, and and answer some really hard questions. Can we really program biology, and if so how? What are the implications of designer babies? Should humans live forever? I really put my heart into this video, I hope you find it useful and inspiring. Enjoy!

Code for this video:
https://github.com/llSourcell/Elevation

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More learning resources:
https://github.com/topics/synthetic-biology?l=python
https://discoverysedge.mayo.edu/2018/07/24/the-gene-editing-tool-crispr-explained/
https://www.vox.com/2018/7/23/17594864/crispr-cas9-gene-editing
https://ghr.nlm.nih.gov/primer/genomicresearch/genomeediting
https://biopython.org/
https://www.nytimes.com/2018/11/30/world/asia/gene-editing-babies-china.html

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#CRISPR #SirajRaval

9 Blockchain Explained

9.1 Decentralized Chat

http://www.youtube.com/watch?v=vVsIHCTGjsE

Peer to peer chat without using a server? We can do this using Ethereum's whisper library. This is a chat protocol that lets nodes chat with each other directly, no need for a central server. It doesn't even use a centralized routing source to let nodes discover each other like BitTorrent does with trackers, instead it uses a distributed hash table as a tool for decentralized peer discovery. Yes its not completely real-time, but the trade-off allows for true private chat. I'm going to explain how it works and code the demo at the end.

Code for this video:
https://github.com/llSourcell/Decentralized_Chat

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https://ethereum.stackexchange.com/questions/127/what-is-whisper-and-what-is-it-used-for
https://hackernoon.com/our-progress-on-ethpay-encrypted-chatting-via-whisper-6a9550ef036a
https://github.com/ethereum/go-ethereum/wiki/How-to-Whisper
https://github.com/ethereum/wiki/wiki/Whisper

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9.2 Learn Blockchain Programming (curriculum)

http://www.youtube.com/watch?v=wVVGv2bmxow

Blockchain technology is hot right now! I've devised a 2 month study plan to help you learn how blockchains and cryptocurrency works. This is the kind of curriculum I'd create for myself to learn, but I'm open sourcing it for you guys since I love you. We'll start with cryptography, move on to Bitcoin, then Ethereum, other cryptos, and finally Decentralized Applications.

Curriculum for this video:
https://github.com/llSourcell/Learn_Blockchain_in_2_months

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Week 1 (Cryptography):
https://www.coursera.org/learn/crypto

Week 2 (Mastering Bitcoin + Annotated BTC Whitepaper + BTC wallet)
https://github.com/bitcoinbook/bitcoinbook
http://fermatslibrary.com/s/bitcoin
https://bitcoin.org/en/choose-your-wallet

Week 3 (Bitcoin and Cryptocurrencies + build a blockchain)
https://www.coursera.org/learn/cryptocurrency
https://hackernoon.com/learn-blockchains-by-building-one-117428612f46

Week 4 (Ethereum + Annotated Eth Whitepaper):
https://www.udemy.com/blockchain-application/
http://fermatslibrary.com/s/ethereum-a-next-generation-smart-contract-and-decentralized-application-platform#email-newsletter

Week 5 (Solidity Programming + Ethereum articles):
https://cryptozombies.io/
https://blockgeeks.com/?s=ethereum

Week 6 (Other Cryptocurrencies):
https://www.youtube.com/watch?v=cjbHqvr4ffo&list=PL2-dafEMk2A7jW7CYUJsBu58JH27bqaNL

Week 7 (Decentralized Applications):
http://shop.oreilly.com/product/0636920039334.do

Week 8 (Build a Decentralized Application):
https://github.com/moshest/p2p-index

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9.3 Web 3.0 Explained

http://www.youtube.com/watch?v=aPVmd7SyKfQ

Welcome to Web 3.0! I'm going to cover what Web 3.0 is, how a blockchain works (visually), what new kinds of apps are now possible, and at the end we'll write our first smart contract. This video is apart of the Decentralized Applications course found at www.theschool.ai

Code for this video:
https://github.com/llSourcell/Web3.0_Explained

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More learning resources:
https://blockgeeks.com/guides/what-is-cryptocurrency/
https://blog.ethereum.org/author/vitalik-buterin/
https://medium.com/@VitalikButerin
https://codeburst.io/build-your-first-ethereum-smart-contract-with-solidity-tutorial-94171d6b1c4b

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9.4 Decentralized Applications

http://www.youtube.com/watch?v=VyQVlBQCX_Y

Learn more and join the program at https://www.theschool.ai

Wizards I'm so excited to be able to launch this finally! Artificial Intelligence and blockchain technology have enabled a new breed of application software popularly called 'Decentralized Apps'. I wrote the popular O'Reilly book titled 'Decentralized Applications" and now I'm creating a brand new online course to teach you how to build your own Decentralized app using blockchains, distributed hash tables, peer to peer protocols, and deep learning! This is the first course in The School of AI and the students that sign up for it will receive weekly videos, live streams, Q&A office hours, exclusive projects, a certificate of completion, expert feedback and review to teach the foundations of this future-shaping technology.
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9.5 Blockchain Consensus Algorithms and Artificial Intelligence

http://www.youtube.com/watch?v=5Tr13l0O1Ws

Is blockchain + AI a winning combo? Yes! They are complementary technologies, and knowing how both work will make you a much more powerful developer. Artificial Intelligence can use the power of the blockchain to audit data, add incentives to its goals, and even create new types of meritocratic organizations. In this video, i'll talk about how they can both work together, code out the proof of work algorithm in python, then talk about a few other consensus algorithms at a high level.

Code for this video:
https://github.com/llSourcell/blockchain_consensus

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More Learning Resources:
https://blog.bigchaindb.com/blockchains-for-artificial-intelligence-ec63b0284984
https://www.topbots.com/combination-ai-blockchain-revolutionize-10-industries/
https://blog.oceanprotocol.com/from-ai-to-blockchain-to-data-meet-ocean-f210ff460465
https://www.slideshare.net/bicalabs/artificial-intelligence-blockchain-synergy
https://blog.ethereum.org/2014/05/06/daos-dacs-das-and-more-an-incomplete-terminology-guide/
https://bitcoinmagazine.com/articles/bootstrapping-a-decentralized-autonomous-corporation-part-i-1379644274/
https://www.wired.com/2016/06/50-million-hack-just-showed-dao-human/

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9.6 An Introduction to The Interplanetary File System

http://www.youtube.com/watch?v=BA2rHlbB5i0

Towards the Permanent Web! HTTP has served us well, but its time to upgrade the way the Internet works. IPFS provides a solution for the ills of HTTP. It content addresses data instead of location addressing it, and provides more bandwidth, better latency, and more resiliency. We'll build a simple video streaming web app using IPFS!

Code for this video:
https://github.com/llSourcell/IPFS_Demo

Tushar's Winning Code:
https://github.com/OpenMined/PySyft/pull/268/commits

Rohan's Runner-up Code:
https://github.com/OpenMined/PySyft/pull/273

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More learning Resources:
https://ipfs.io/docs/examples/
https://flyingzumwalt.gitbooks.io/decentralized-web-primer/content/files-on-ipfs/
https://github.com/INFURA/tutorials/wiki/Introduction-to-IPFS
https://www.youtube.com/watch?v=jONZtXMu03w&t=341s
https://ethereum.stackexchange.com/questions/7664/how-can-we-integrate-ipfs-with-ethereum-in-dapps
https://mlgblockchain.com/intro-ipfs.html
https://medium.com/@ConsenSys/an-introduction-to-ipfs-9bba4860abd0

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https://www.patreon.com/user?u=3191693
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9.7 How Does Blockchain Work

http://www.youtube.com/watch?v=LZEHOlZY2To

In this video, you'll learn how the blockchain works. You won't find this data structure in your computer science textbooks yet, but it will soon underpin the way the entire Internet operates. Let's talk about how blockchain works and how it can be used to improve our AI.

Code for this video:
https://github.com/llSourcell/The_Power_of_the_blockchain

Charles-David's winning code:
https://github.com/alkaya/Optimizer-cotw

Parminder's runner up code:
https://github.com/Trion129/Gradient-Descent-from-scratch

Please subscribe! And like. And comment. That's what keeps me going.

More learning resources:
https://blog.bigchaindb.com/blockchains-for-artificial-intelligence-ec63b0284984
https://medium.com/towards-data-science/the-blockchain-and-ai-fbfa691f10e0
https://medium.com/the-intrepid-review/how-does-the-blockchain-work-for-dummies-explained-simply-9f94d386e093
https://www.oreilly.com/ideas/understanding-the-blockchain
https://github.com/golemfactory/golem

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9.8 3 Dapps You HAVE to See

http://www.youtube.com/watch?v=vCBCyO7SE5I

La'Zooz: http://www.lazooz.net/
OpenBazaar: https://openbazaar.org/
Synereo: http://www.synereo.com

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

My Book on DApps: http://www.amazon.com/Decentralized-Applications-Harnessing-Blockchain-Technology/dp/1491924543

Awesome Paper on DApps: https://github.com/DavidJohnstonCEO/DecentralizedApplications

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
https://www.patreon.com/user?ty=h&u=3191693
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9.9 How do we Democratize Access to Data?

http://www.youtube.com/watch?v=HAC6sqq7_-U

OpenMined is a community focused on building technology for decentralized ownership of data and AI. Data scientists can pay users directly for their data and train AI models in a decentralized way. We'll cover deep learning, federated learning, homomorphic encryption, and blockchain smart contracts!

Code for this video:
https://github.com/llSourcell/OpenMined_demo

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More learning resources:
https://openmined.slack.com/
http://openmined.org/
https://github.com/OpenMined/Docs
https://research.googleblog.com/2017/04/federated-learning-collaborative.html
https://www.youtube.com/playlist?list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3
https://www.youtube.com/watch?v=LZEHOlZY2To&t=8s
https://www.youtube.com/watch?v=gSQXq2_j-mw

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10 Learning Curriculums

10.1 Computer Science Curriculum

http://www.youtube.com/watch?v=-OvRVlqKebI

Are you too busy to dedicate 4 years of your life to a traditional Computer Science Major? I've created a 5 month accelerated Computer Science curriculum to help you get a broad overview of the field, covering the most important topics in sequential order using the free resources of the Internet. I've listed learning tips, Computer Scientists to follow, and a path in this video. I hope you find it useful, this is the kind of learning path I'd design for myself but I'm open sourcing it. Enjoy!

Curriculum for this video:
https://github.com/llSourcell/Learn_Computer_Science_in_5_Months

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Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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instagram: https://www.instagram.com/sirajraval

People to follow on Twitter:
Jeff Dean
Paul Allen
Tim Berners-Lee
Linus Torvalds
Brendan Eich
John Carmack

Curriculum:
Week 1-2 (Learn Python)
- https://automatetheboringstuff.com/
- https://www.codecademy.com/learn/learn-python

Week 3-4 (Data Structures)
- https://www.edx.org/course/data-structures-fundamentals-uc-san-diegox-algs201x

Week 5-6 (Algorithms)
- https://courses.csail.mit.edu/6.006/fall11/notes.shtml

Week 7 (Databases)
- https://www.coursera.org/learn/python-databases

Week 8 (Networking)
- https://www.coursera.org/learn/computer-networking

Week 9-10 (Web Development)
- https://www.youtube.com/watch?v=1u2qu-EmIRc&list=PLhQjrBD2T382hIW-IsOVuXP1uMzEvmcE5
- https://github.com/melanierichards/just-build-websites

Week 11-12 (Mobile Development)
- https://developer.apple.com/library/content/referencelibrary/GettingStarted/DevelopiOSAppsSwift/
- https://developer.android.com/training/basics/firstapp/index.html

Week 13-14 (Data Science)
- https://www.edx.org/course/python-for-data-science

Week 15-16 (Computer Vision)
- https://www.coursera.org/learn/python-text-mining

Week 17-18 (Natural Language Processing)
- https://www.udacity.com/course/introduction-to-computer-vision--ud810

Week 19 (Software Engineering Practices)
- https://www.coursera.org/learn/software-processes

Week 20 (Blockchain)
- https://www.youtube.com/watch?v=cjbHqvr4ffo&list=PL2-dafEMk2A7jW7CYUJsBu58JH27bqaNL

Sign up for the next course at The School of AI:
http://www.theschool.ai

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https://www.patreon.com/user?u=3191693
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10.2 Learn Blockchain Programming (curriculum)

http://www.youtube.com/watch?v=wVVGv2bmxow

Blockchain technology is hot right now! I've devised a 2 month study plan to help you learn how blockchains and cryptocurrency works. This is the kind of curriculum I'd create for myself to learn, but I'm open sourcing it for you guys since I love you. We'll start with cryptography, move on to Bitcoin, then Ethereum, other cryptos, and finally Decentralized Applications.

Curriculum for this video:
https://github.com/llSourcell/Learn_Blockchain_in_2_months

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Twitter: https://twitter.com/sirajraval
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instagram: https://www.instagram.com/sirajraval

Sign up for the next course at The School of AI:
http://theschool.ai/

Week 1 (Cryptography):
https://www.coursera.org/learn/crypto

Week 2 (Mastering Bitcoin + Annotated BTC Whitepaper + BTC wallet)
https://github.com/bitcoinbook/bitcoinbook
http://fermatslibrary.com/s/bitcoin
https://bitcoin.org/en/choose-your-wallet

Week 3 (Bitcoin and Cryptocurrencies + build a blockchain)
https://www.coursera.org/learn/cryptocurrency
https://hackernoon.com/learn-blockchains-by-building-one-117428612f46

Week 4 (Ethereum + Annotated Eth Whitepaper):
https://www.udemy.com/blockchain-application/
http://fermatslibrary.com/s/ethereum-a-next-generation-smart-contract-and-decentralized-application-platform#email-newsletter

Week 5 (Solidity Programming + Ethereum articles):
https://cryptozombies.io/
https://blockgeeks.com/?s=ethereum

Week 6 (Other Cryptocurrencies):
https://www.youtube.com/watch?v=cjbHqvr4ffo&list=PL2-dafEMk2A7jW7CYUJsBu58JH27bqaNL

Week 7 (Decentralized Applications):
http://shop.oreilly.com/product/0636920039334.do

Week 8 (Build a Decentralized Application):
https://github.com/moshest/p2p-index

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http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693
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10.3 Learn Machine Learning in 3 Months (with curriculum)

http://www.youtube.com/watch?v=Cr6VqTRO1v0

How is a total beginner supposed to get started learning machine learning? I'm going to describe a 3 month curriculum to help you go from beginner to well-versed in machine learning. Its an accelerated learning plan, something i'd create for myself if I were to get started today, but I'm going to open source it for you guys. This curriculum will cover all the math concepts, the machine learning theory, and the deep learning theory to get you up to speed with the field as fast as possible. If anyone asks how to best get started with machine learning, direct them to this video!

Curriculum from this video:
https://github.com/llSourcell/Learn_Machine_Learning_in_3_Months

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Month 1

Week 1 Linear Algebra
https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/
Week 2 Calculus
https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr
Week 3
https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2
Week 4 Algorithms
https://www.coursera.org/courses?languages=en&query=Algorithm%20design%20and%20analysis

Month 2

Week 1
learn python for data science
https://www.youtube.com/watch?v=T5pRlIbr6gg&list=PL2-dafEMk2A6QKz1mrk1uIGfHkC1zZ6UU
Math of Intelligence
https://www.youtube.com/watch?v=xRJCOz3AfYY&list=PL2-dafEMk2A7mu0bSksCGMJEmeddU_H4D
Intro to Tensorflow
https://www.youtube.com/watch?v=2FmcHiLCwTU&list=PL2-dafEMk2A7EEME489DsI468AB0wQsMV

Week 2
Intro to ML (Udacity)
https://eu.udacity.com/course/intro-to-machine-learning--ud120

Week 3-4
ML Project Ideas
https://github.com/NirantK/awesome-project-ideas

Month 3 (Deep Learning)

Week 1
Intro to Deep Learning
https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3

Week 2
Deep Learning by Fast.AI
http://course.fast.ai/

Week 3-4
Re-implement DL projects from my github
https://github.com/llSourcell?tab=repositories

ML people to follow on Twitter:
https://www.quora.com/Who-should-I-follow-on-Twitter-to-get-useful-and-reliable-machine-learning-information


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http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693
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10.4 Learn Deep Learning in 6 Weeks

http://www.youtube.com/watch?v=_qjNH1rDLm0

Deep Learning is the dark art of our times. Incredibly powerful, mysteriously accurate, and accessible to just about anyone. In this video, i've compiled an open source 6 week curriculum to help you learn deep learning using various sources from the Web. I'll describe all of my learning resources, why i chose them, and how they can help you. Starting with feedforward networks, to convolutional networks, recurrent networks, adversarial learning, and finally deep reinforcement learning. Enjoy!

Curriculum for this video:
https://github.com/llSourcell/Learn_Deep_Learning_in_6_Weeks/

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Want more education? Connect with me here:
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http://wizards.herokuapp.com/

Sign up for the next course at The School of AI:
https://www.theschool.ai

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https://www.patreon.com/user?u=3191693
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11 Cryptocurrencies

11.1 How Does Monero Work?

http://www.youtube.com/watch?v=cjbHqvr4ffo

Monero is a cryptocurrency that enables private transactions. That means the sender, receiver, and transaction amount are not publicly viewable, unlike Bitcoin's blockchain. Monero's transactions stay private using the technology of ring signatures, ringCT, stealth addresses, and I2P routing. I'll explain how all of this works in this video. I do not condone the use of this technology for illegal transactions. This is powerful stuff, ideally, we start using this as a stepping stone towards a world where we get paid for our transactional data (and all the rest of our data).

You'll find the jupyter notebook for this video and the associated code that I demo in the github link below.

Code for this video:
https://github.com/llSourcell/how_does_monero_work

More learning resources:
https://getmonero.org/
https://github.com/monero-project/monero
https://www.monero.how/tutorial-how-to-use-the-monero-gui-wallet
https://www.monero.how/tutorial-how-to-mine-monero
https://99bitcoins.com/beginners-guide-to-monero/

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Want more inspiration & education? Connect with me:
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11.2 How does IOTA work?

http://www.youtube.com/watch?v=B37UbzPlSzw

Blockchains are so 2017. 2018 will be all about BlockDAGs. I'll explain a cryptocurrency system called IOTA that is getting really popular and how it differs from a traditional blockchain. At the end I'll show you how you can buy some.

Code for this video:
https://github.com/llSourcell/IOTA_demo

More learning resources:
https://learn.iota.org/tutorials
https://learn.iota.org/tutorial/payments-and-messaging-leaderboard
https://www.youtube.com/watch?v=MsaPA3U4ung
https://iotasupport.com/buyingiotaotc.shtml
https://iotasupport.com/gettingstarted.shtml

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Want more inspiration & education? Connect with me:
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Facebook: https://www.facebook.com/sirajology


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11.3 What are Blockchain Smart Contracts?

http://www.youtube.com/watch?v=dP0-8D2fSb8

More and more apps will start using smart contract technology to enable never before possible features. We're going to build a smart contract called "proof of existence" that acts as a digital notary for any document using the Ethereum blockchain.

Code for this video:
https://github.com/llSourcell/proof_of_existence_demo

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More learning resources:
https://www.slideshare.net/intrins1k/ethereum-meetup-presentation-01042017-70716809
https://www.youtube.com/watch?v=R_CiemcFKis
https://auth0.com/blog/an-introduction-to-ethereum-and-smart-contracts-part-2/
https://ethereumdev.io/
https://ethereum.gitbooks.io/frontier-guide/content/writing_contract.html
http://hypernephelist.com/2016/06/01/deploying-my-first-smart-contract.html
https://blog.cloudboost.io/ethereum-smart-contracts-in-a-nutshell-for-hackers-64f357715791
http://www.techracers.com/smart-contract-solidity
http://ecomunsing.com/tutorial-controlling-ethereum-with-python

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon: https://www.patreon.com/user?u=3191693 Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
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11.4 Ethereum Explained

http://www.youtube.com/watch?v=-_Qs0XdPpw8

Let's build a decentralized ticket service using Ethereum! Ethereum is the 2nd biggest cryptocurrency in market cap behind Bitcoin and offers a Turing-complete blockchain. Using Ethereum + IPFS, developers can build powerful decentralized applications, and this offers novelty in a somewhat saturated market for app developers. Let's get started!

Code for this video:
https://github.com/llSourcell/ethereum_demo

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Follow Siraj on
Twitter: https://twitter.com/sirajraval

Facebook: https://www.facebook.com/sirajology

More learning resources;
https://www.youtube.com/watch?v=8jI1TuEaTro
https://ethereum.org/greeter
https://dappsforbeginners.wordpress.com/
https://ethereum.stackexchange.com/questions/5952/ethereum-tutorial-for-beginners
http://truffleframework.com/tutorials/ethereum-overview
https://ethereumbuilders.gitbooks.io/guide/content/en/solidity_tutorials.html
https://blockgeeks.com/guides/how-to-learn-solidity/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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11.5 Blockchain Consensus Algorithms and Artificial Intelligence

http://www.youtube.com/watch?v=5Tr13l0O1Ws

Is blockchain + AI a winning combo? Yes! They are complementary technologies, and knowing how both work will make you a much more powerful developer. Artificial Intelligence can use the power of the blockchain to audit data, add incentives to its goals, and even create new types of meritocratic organizations. In this video, i'll talk about how they can both work together, code out the proof of work algorithm in python, then talk about a few other consensus algorithms at a high level.

Code for this video:
https://github.com/llSourcell/blockchain_consensus

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More Learning Resources:
https://blog.bigchaindb.com/blockchains-for-artificial-intelligence-ec63b0284984
https://www.topbots.com/combination-ai-blockchain-revolutionize-10-industries/
https://blog.oceanprotocol.com/from-ai-to-blockchain-to-data-meet-ocean-f210ff460465
https://www.slideshare.net/bicalabs/artificial-intelligence-blockchain-synergy
https://blog.ethereum.org/2014/05/06/daos-dacs-das-and-more-an-incomplete-terminology-guide/
https://bitcoinmagazine.com/articles/bootstrapping-a-decentralized-autonomous-corporation-part-i-1379644274/
https://www.wired.com/2016/06/50-million-hack-just-showed-dao-human/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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11.6 What is an Initial Coin Offering?

http://www.youtube.com/watch?v=iyuZ_bCQeIE

Let's cut through the hype and understand Initial Coin Offerings (ICOs) by creating one ourselves programmatically! We'll first learn about Bitcoin & Ethereums architecture to prepare us for the smart contract creation process. Our DemoCoin ICO will be built using tools from the Ethereum developer ecosystem.

Code for this video:
https://github.com/llSourcell/what_is_an_initial_coin_offering

Alberto's Winning Code:
https://github.com/alberduris/The_Math_of_Intelligence/tree/master/Week10

Eric's 2nd place Code:
https://github.com/EricAlcaide/Math_of_Intelligence/tree/master/Quantum_Computing

Please Subscribe! And like. And comment.

More learning resources:
https://medium.com/startup-grind/hack-your-funding-with-an-initial-coin-offering-2a2a0614bddf
https://blog.zeppelin.solutions/how-to-create-token-and-initial-coin-offering-contracts-using-truffle-openzeppelin-1b7a5dae99b6
https://bitsonblocks.net/2017/04/25/a-gentle-introduction-to-initial-coin-offerings-icos/
https://blockchainhub.net/ico-initial-coin-offerings/
https://medium.com/@mvmurthy/full-stack-hello-world-voting-ethereum-dapp-tutorial-part-1-40d2d0d807c2
https://medium.com/@ConsenSys/a-101-noob-intro-to-programming-smart-contracts-on-ethereum-695d15c1dab4
https://www.ethereum.org/greeter

I wrote a book on this stuff last year (most of the code is now deprecated since the space moves very fast, but the theories still hold true) so I'm very excited to see this space finally coming to fruition:
http://shop.oreilly.com/product/0636920039334.do

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11.7 A Guide to Building Your First Decentralized Application

http://www.youtube.com/watch?v=gSQXq2_j-mw

Web 3.0 is here! Welcome to the dark side of web and mobile development (in a good way). Lets talk about how we can use blockchains, distributed hash tables, and peer to peer protocols to create decentralized applications!

Code for this video:
https://github.com/llSourcell/Your_First_Decentralized_Application

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More learning resources:
https://www.safaribooksonline.com/library/view/decentralized-applications/9781491924532/ch01.html
https://github.com/DavidJohnstonCEO/DecentralizedApplications
https://coinsutra.com/dapps-decentralized-applications/
https://medium.com/@mvmurthy/full-stack-hello-world-voting-ethereum-dapp-tutorial-part-1-40d2d0d807c2
https://blockgeeks.com/guides/dapps-the-decentralized-future/
https://dappsforbeginners.wordpress.com/tutorials/your-first-dapp/
https://ethereum.stackexchange.com/questions/122/how-to-create-a-dapp-from-scratch-on-ethereum
https://blog.coinbase.com/app-coins-and-the-dawn-of-the-decentralized-business-model-8b8c951e734f

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Credits to Mahesh Murthy for the code and inspiration: http://www.zastrin.com
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11.8 How Does Blockchain Work

http://www.youtube.com/watch?v=LZEHOlZY2To

In this video, you'll learn how the blockchain works. You won't find this data structure in your computer science textbooks yet, but it will soon underpin the way the entire Internet operates. Let's talk about how blockchain works and how it can be used to improve our AI.

Code for this video:
https://github.com/llSourcell/The_Power_of_the_blockchain

Charles-David's winning code:
https://github.com/alkaya/Optimizer-cotw

Parminder's runner up code:
https://github.com/Trion129/Gradient-Descent-from-scratch

Please subscribe! And like. And comment. That's what keeps me going.

More learning resources:
https://blog.bigchaindb.com/blockchains-for-artificial-intelligence-ec63b0284984
https://medium.com/towards-data-science/the-blockchain-and-ai-fbfa691f10e0
https://medium.com/the-intrepid-review/how-does-the-blockchain-work-for-dummies-explained-simply-9f94d386e093
https://www.oreilly.com/ideas/understanding-the-blockchain
https://github.com/golemfactory/golem

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11.9 Simple Token in 5 Minutes

http://www.youtube.com/watch?v=1kugO8xMQmw

Learn how to create your own simple token in about 5 minutes wth the help of this video! I'm gong to show you how to create a cryptocurrency using Ethereum's smart contract platform and the ERC20 token creation protocol. This is currently the most popular way to create tokens and you can get this whole thing up and running in about an hour. I'll go over some relevant tools like MetaMask and the remix compiler as well.

Code for this video:
https://github.com/llSourcell/simple_token

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More learning resources:
https://www.ethereum.org/token
https://steemit.com/ethereum/@maxnachamkin/how-to-create-your-own-ethereum-token-in-an-hour-erc20-verified
https://ether.direct/2017/08/22/ethereum-beginners-guide-create-a-simple-cryptocurrency/
https://medium.com/simple-token/simple-token-prepares-flying-start-to-2018-12154670dae9
https://simpletoken.org/
https://hashnode.com/post/how-to-build-your-own-ethereum-based-erc20-token-and-launch-an-ico-in-next-20-minutes-cjbcpwzec01c93awtbij90uzn

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11.10 Crypto Math

http://www.youtube.com/watch?v=tK3wuQN9MHE

The math behind cryptography is immensely fascinating, I could spend all day studying it! We're going to go over some fundamental cryptographic concepts like hashing, zero knowledge proofs, and my favorite 'ZK-Snarks'. This is quite an in-depth video, i had to pick and choose the topics i wanted to dive into more. There is so, so much i could talk about. Each of these topics could deserve their own course. Cryptography is going to be paramount to building future decentralized Artificial Intelligence systems that we can both control and protect from attackers.

Code for this video:
https://github.com/llSourcell/crypto_math/blob/master/Crypto%20Math.ipynb

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More learning resources:
https://www.tutorialspoint.com/cryptography/
https://gpgtools.tenderapp.com/kb/how-to/introduction-to-cryptography
https://www.khanacademy.org/computing/computer-science/cryptography
https://www.ibm.com/developerworks/tivoli/tutorials/s-crypto/s-crypto.html
https://openmined.org/

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http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693
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11.11 Ethereum Future Price Prediction

http://www.youtube.com/watch?v=G5Mx7yYdEhE

Can we predict cryptocurrency prices using machine learning? We're going to build a Keras deep learning model that attemps to predict the future price of cryptocurrencies like Bitcoin and Ethereum in this video. The type of model i'm using is a bidirectional LSTM recurrent network. Ethereum future prices as well as other cryptocurrency prices are hard to predict, but with the power of machine learning we can find a suitable prediction.

Code for this video:
https://github.com/llSourcell/ethereum_future

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instagram: https://www.instagram.com/sirajraval

More learning resources:
https://dashee87.github.io/deep%20learning/python/predicting-cryptocurrency-prices-with-deep-learning/
https://nicholastsmith.wordpress.com/2017/11/13/cryptocurrency-price-prediction-using-deep-learning-in-tensorflow/
https://github.com/PiSimo/BitcoinForecast
https://github.com/philipperemy/deep-learning-bitcoin

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http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693
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11.12 Crypto Game Tokens

http://www.youtube.com/watch?v=3rCtOROuPNw

Cryptocurrency and video game assets? There is indeed an intersection, the emerging esports industry is growing rapidly. Cryptocurrency offers a solution to gamers to allow them to spend their in-game currency outside of the game world. In this video, I'll talk about several cryptocurrencies that are fueling the esports movement and in the process break down how the proof of stake consensus algorithm works as well as the token distribution algorithm in Solidity.

Slides for this video:
https://github.com/llSourcell/Crypto_game_tokens

Enroll in The School of AI:
https://www.theschool.ai/courses/decentralized-applications

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More Learning resources:
https://www.xpcoin.io/
https://blockonomi.com/gaming-esports-cryptocurrencies/
https://wax.io/
https://icoinblog.com/top-3-esports-altcoins-cryptocurrency-tokens-2018/

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http://wizards.herokuapp.com/

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12 Introduction to AI for Video Games (Reinforcement Learning)

12.1 Introduction to AI for Video Games

http://www.youtube.com/watch?v=i_McNBDP9Qs

Welcome to my new reinforcement learning course! For the next 10 weeks we're going to go from the basics to the state of the art in this popular subfield of machine learning using video game environments as our testbed. RL is a huge reason DeepMind and OpenAI have been so successful thus far in creating world changing AI bots. Make sure to subscribe so you'll get updated with every new video I release. And don't worry if you don't understand policy iteration or value iteration just yet, I merely wanted to introduce these phrases in this video, next week i'm going to really dive into what these 2 methods look like programmatically.

Code for this video (with coding challenge):
https://github.com/llSourcell/AI_for_video_games_demo

Syllabus for this course:
https://github.com/llSourcell/AI_for_Video_Games_Syllabus

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More learning resources:
https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0
http://icml.cc/2016/tutorials/deep_rl_tutorial.pdf
https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow
https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/
https://web.mst.edu/~gosavia/tutorial.pdf
http://karpathy.github.io/2016/05/31/rl/
http://www.wildml.com/2016/10/learning-reinforcement-learning/
https://www.quora.com/What-are-some-good-tutorials-on-reinforcement-learning

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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12.2 Navigating a Virtual World Using Dynamic Programming

http://www.youtube.com/watch?v=5R2vErZn0yw

Let's teach our AI how to get from point A to point B of a Frozen Lake environment in the most efficient way possible using dynamic programming. This is considered reinforcement learning and we'll trying two popular techniques (policy iteration and value iteration). We'll use OpenAI's Gym environment and pure python to do this.

Code for this video:
https://github.com/llSourcell/navigating_a_virtual_world_with_dynamic_programming

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More learning resources:
https://ocw.mit.edu/courses/aeronautics-and-astronautics/16-410-principles-of-autonomy-and-decision-making-fall-2010/lecture-notes/MIT16_410F10_lec23.pdf
http://uhaweb.hartford.edu/compsci/ccli/projects/QLearning.pdf
https://medium.com/@m.alzantot/deep-reinforcement-learning-demysitifed-episode-2-policy-iteration-value-iteration-and-q-978f9e89ddaa
https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a-html/node19.html
http://cs.stanford.edu/people/karpathy/reinforcejs/gridworld_dp.html
https://www.quora.com/How-is-policy-iteration-different-from-value-iteration
http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/DP.pdf

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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12.3 Monte Carlo Prediction

http://www.youtube.com/watch?v=-YpalutQCKw

We're going to program a virtual robot to do some house cleaning for us using a technique called monte carlo prediction. i'm going to explain what it is, how it works and how we can use it for reinforcement learning.

Code for this video:
https://github.com/llSourcell/navigating_a_virtual_world_with_dynamic_programming

Justin's Winning code:
https://github.com/wagonhelm/Value-Iteration

Sakcham's runner up code:
https://github.com/sakchhams/pacman_ai

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More learning resources:
https://ocw.mit.edu/courses/aeronautics-and-astronautics/16-410-principles-of-autonomy-and-decision-making-fall-2010/lecture-notes/MIT16_410F10_lec23.pdf
http://uhaweb.hartford.edu/compsci/ccli/projects/QLearning.pdf
https://medium.com/@m.alzantot/deep-reinforcement-learning-demysitifed-episode-2-policy-iteration-value-iteration-and-q-978f9e89ddaa
https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a-html/node19.html
http://cs.stanford.edu/people/karpathy/reinforcejs/gridworld_dp.html
https://www.quora.com/How-is-policy-iteration-different-from-value-iteration
http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/DP.pdf

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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12.4 Q Learning Explained (tutorial)

http://www.youtube.com/watch?v=aCEvtRtNO-M

Can we train an AI to complete it's objective in a video game world without needing to build a model of the world before hand? The answer is yes using Q learning! I'll go through several use cases and show some python code of how Q learning works.

Code for this video:
https://github.com/llSourcell/Q_Learning_Explained/

Adnan's Winning code:
https://github.com/AdnanZahid/ReinforcementLearning

Alberto's runner up code:
https://github.com/alberduris

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More learning resources:
http://mnemstudio.org/path-finding-q-learning-tutorial.htm
https://ocw.mit.edu/courses/aeronautics-and-astronautics/16-410-principles-of-autonomy-and-decision-making-fall-2010/lecture-notes/MIT16_410F10_lec23.pdf
http://uhaweb.hartford.edu/compsci/ccli/projects/QLearning.pdf
https://medium.com/@m.alzantot/deep-reinforcement-learning-demysitifed-episode-2-policy-iteration-value-iteration-and-q-978f9e89ddaa
https://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a-html/node19.html
http://cs.stanford.edu/people/karpathy/reinforcejs/gridworld_dp.html
https://www.quora.com/How-is-policy-iteration-different-from-value-iteration
http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/DP.pdf

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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12.5 Solving the Basic Game of Pong

http://www.youtube.com/watch?v=pN7ETkOizGM

Building an AI to beat pong using just the pixels of the screen as input with no hard-coded rules? Yes, its possible. We'll solve this using an approach called "Policy Gradients" which is even more popular than Q-learning. I'll show you how its done using a mix of animations, code, and theory. Let's beat pong!

Code (and challenge) for this week:
https://github.com/llSourcell/policy_gradients_pong

Alex's Winning code:
https://github.com/msoedov/q-learner

Aditya's Runner up code:
https://github.com/avp1598/q_learning

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Want more inspiration & education? Connect with me:
Twitter: https://twitter.com/sirajraval

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More learning resources:
http://karpathy.github.io/2016/05/31/rl/
https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-2-ded33892c724
http://minpy.readthedocs.io/en/latest/tutorial/rl_policy_gradient_tutorial/rl_policy_gradient.html
http://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html
http://kvfrans.com/simple-algoritms-for-solving-cartpole/
https://theneuralperspective.com/2016/11/25/reinforcement-learning-rl-policy-gradients-i/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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12.6 Actor Critic Algorithms

http://www.youtube.com/watch?v=w_3mmm0P0j8

Reinforcement learning is hot right now! Policy gradients and deep q learning can only get us so far, but what if we used two networks to help train and AI instead of one? Thats the idea behind actor critic algorithms. I'll explain how they work in this video using the 'Doom" shooting game as an example.

Code for this video:
https://github.com/llSourcell/actor_critic

i-Nickk's winning code:
https://github.com/I-NicKK/Tic-Tac-Toe

Vignesh's runner up code:
https://github.com/tj27-vkr/Q-learning-conv-net

Taryn's Twitter:
https://twitter.com/tarynsouthern

More learning resources:
https://papers.nips.cc/paper/1786-actor-critic-algorithms.pdf
http://rll.berkeley.edu/deeprlcourse/f17docs/lecture_5_actor_critic_pdf.pdf
http://web.mit.edu/jnt/www/Papers/J094-03-kon-actors.pdf
http://mlg.eng.cam.ac.uk/rowan/files/rl/06_actorcritic.pdf
http://mi.eng.cam.ac.uk/~mg436/LectureSlides/MLSALT7/L5.pdf

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12.7 Military Robots

http://www.youtube.com/watch?v=tm5kQmjfZN8

What do advancements in AI mean for the military? Military robotics has come a long way with advancements in machine learning, the soaring affordability of computing power, and the rise of cloud computing. I'll talk about how AI is used on the battlefield and how we can prevent a SkyNet scenario from occuring.

Code for this video:
https://github.com/llSourcell/proximal_policy_optimization

More learning resources:
https://www.robotictechnologyinc.com/images/upload/file/Presentation%20Military%20Memetics%20Tutorial%2013%20Dec%2011.pdf
http://faculty.cse.tamu.edu/murphy/IROS2011Tutorial.htm
https://insights.sei.cmu.edu/sei_blog/2017/06/army-robotics-in-the-military.html
https://www.technologyreview.com/s/603795/the-us-military-wants-its-autonomous-machines-to-explain-themselves/
https://gcn.com/articles/2017/05/22/dod-ai-machine-learning.aspx
https://www.engadget.com/2017/05/15/the-pentagon-is-hunting-isis-using-big-data-and-machine-learning/

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13 Art Generation

13.1 Build an AI Artist - Machine Learning for Hackers #5

http://www.youtube.com/watch?v=9Mxw_ilpvwA

This video will get you up and running with your first AI Artist using the deep learning library Keras!

The code for this video is here:
https://github.com/llSourcell/AI_Artist

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Here's the initial Google DeepDream blog post:
http://googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-into-neural.html

A Deepdream web app:
https://dreamscopeapp.com/

The Neural Style Paper:
http://arxiv.org/pdf/1508.06576v2.pdf

Some great info on convolutional neural networks:
http://colah.github.io/posts/2014-07-Conv-Nets-Modular/

You should train this baby in the cloud using AWS. See ML for Hackers #4 for a tutorial on how to use AWS:
https://www.youtube.com/watch?v=eKmIVU8EUbw

This person went ahead and made a web app so you don't even have to compile the code to try this out:
https://deepart.io/

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment.
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13.2 Deep Dream in TensorFlow - Learn Python for Data Science #5

http://www.youtube.com/watch?v=MrBzgvUNr4w

In this video, we replicate Google's Deep Dream code in 80 lines of Python using the Tensorflow machine learning library. Then we visualize it at the end.

The challenge for this video is here:
https://github.com/llSourcell/deep_dream_challenge

Avhirup's winning stock prediction code:
https://github.com/Avhirup/Stock-Market-Prediction-Challenge

Victor's runner-up code:
https://github.com/ciurana2016/predict_stock_py

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

More Deep Dream tutorials:

http://www.alanzucconi.com/2016/05/25/generating-deep-dreams/
https://github.com/awanninger/deepdream
http://ryankennedy.io/running-the-deep-dream/

Generate Deep Dream's online:
http://deepdreamgenerator.com/generator-style

Still my favorite intro to neuroscience class:
https://www.mcb80x.org/

Please subscribe! And share this video, like + comment. That's what keeps me going.

Please support me on Patreon if you like my videos:
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13.3 How to Generate Art - Intro to Deep Learning #8

http://www.youtube.com/watch?v=Oex0eWoU7AQ

We're going to learn how to use deep learning to convert an image into the style of an artist that we choose. We'll go over the history of computer generated art, then dive into the details of how this process works and why deep learning does it so well.

Coding challenge for this video:
https://github.com/llSourcell/How-to-Generate-Art-Demo

Itai's winning code:
https://github.com/etai83/lstm_stock_prediction

Andreas' runner up code:
https://github.com/AndysDeepAbstractions/How-to-Predict-Stock-Prices-Easily-Demo/blob/master/stockdemo.ipynb

More learning resources:
https://harishnarayanan.org/writing/artistic-style-transfer/
https://ml4a.github.io/ml4a/style_transfer/
http://genekogan.com/works/style-transfer/
https://arxiv.org/abs/1508.06576
https://jvns.ca/blog/2017/02/12/neural-style/

Style transfer apps:
http://www.pikazoapp.com/
http://deepart.io/
https://artisto.my.com/
https://prisma-ai.com/

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https://www.patreon.com/user?u=3191693

Song at the beginning is called Everyday by Carly Comando
jurassic park inception visualization is from http://www.pyimagesearch.com/2015/07/06/bat-country-an-extendible-lightweight-python-package-for-deep-dreaming-with-caffe-and-convolutional-neural-networks/
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13.4 How to Do Style Transfer with Tensorflow (LIVE)

http://www.youtube.com/watch?v=YoBEGQD3LCc

We're going to learn about all the details of style transfer (especially the math) using just Tensorflow. The goal of this session is for you to understand the details behind how style+content loss is calculated and minimized. We'll also talk about future discoveries.

Code for this video:
https://github.com/llSourcell/How_to_do_style_transfer_in_tensorflow

Learning resources:
http://www.makeuseof.com/tag/create-neural-paintings-deepstyle-ubuntu/
https://blog.paperspace.com/art-with-neural-networks/
https://www.tensorflow.org/versions/r0.11/how_tos/
https://no2147483647.wordpress.com/2015/12/21/deep-learning-for-hackers-with-mxnet-2/
https://code.facebook.com/posts/196146247499076/delivering-real-time-ai-in-the-palm-of-your-hand/
http://kawahara.ca/deep-dreams-and-a-neural-algorithm-of-artistic-style-slides-and-explanations/
http://www.chioka.in/tensorflow-implementation-neural-algorithm-of-artistic-style

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13.5 How to Generate Images - Intro to Deep Learning #14

http://www.youtube.com/watch?v=3-UDwk1U77s

We're going to build a variational autoencoder capable of generating novel images after being trained on a collection of images. We'll be using handwritten digit images as training data. Then we'll both generate new digits and plot out the learned embeddings. And I introduce Bayesian theory for the first time in this series :)

Code for this video:
https://github.com/llSourcell/how_to_generate_images

Mike's Winning Code:
https://github.com/xkortex/how_to_win_slot_machines/blob/master/WallStBandits.ipynb

SG's Runner up Code:
https://github.com/esha-sg/Intro-DeepLearning-Siraj-Week13

Please subscribe! And like. And comment. That's what keeps me going.

2 things
-The embedding visualization at the end would be more spread out if i trained it for more epochs (50 is recommended) but i just used 5.
-The code in the video doesn't fully implement the reparameterization trick (to save space) but check the GitHub repo for details on that.

More Learning resources:
https://jaan.io/what-is-variational-autoencoder-vae-tutorial/
http://kvfrans.com/variational-autoencoders-explained/
http://blog.fastforwardlabs.com/2016/08/12/introducing-variational-autoencoders-in-prose-and.html
http://blog.fastforwardlabs.com/2016/08/22/under-the-hood-of-the-variational-autoencoder-in.html
http://blog.evjang.com/2016/11/tutorial-categorical-variational.html
https://jmetzen.github.io/2015-11-27/vae.html

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13.6 How to Generate Images with Tensorflow (LIVE)

http://www.youtube.com/watch?v=iz-TZOEKXzA

We'll build a Variational Autoencoder using Tensorflow to generate images. We'll go through several examples including digit images and pokemon images. VAE's allow us to generate, compress, denoise, and even fuse images together. They are an incredibly powerful tool and we'll go over the implementation details (math included) in this live session.

Code: https://github.com/llSourcell/how_to_generate_images_with_tensorflow_LIVE

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More Learning resources:
https://arxiv.org/abs/1606.05908
https://github.com/stitchfix/fauxtograph
http://deeplearning.jp/cvae/
https://ift6266h17.wordpress.com/2017/03/26/q3-reparameterization-trick-of-variational-autoencoder/
https://www.quora.com/What-is-the-latent-loss-in-variational-autoencoders
https://www.slideshare.net/ShaiHarel/variational-autoencoder-talk

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13.7 How to Generate Video - Intro to Deep Learning #15

http://www.youtube.com/watch?v=-E2N1kQc8MM

Generative Adversarial Networks. It's time. We're going to use a Deep Convolutional GAN to generate images of the alien language from the movie arrival that we can then stitch together to animate into video. I'll go over the architecture of a GAN and then we'll implement one ourselves!

Code for this video (coding challenge included):
https://github.com/llSourcell/how_to_generate_video

Nemanja's winning code:
https://github.com/Nemzy/video_generator

Niyas' Runner up code:
https://github.com/niazangels/vae-pokedex

and his blog post:
https://hackernoon.com/how-to-autoencode-your-pok%C3%A9mon-6b0f5c7b7d97

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More Learning Resources:
http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/
https://blog.openai.com/generative-models/
http://cs.stanford.edu/people/karpathy/gan/
https://channel9.msdn.com/Events/Neural-Information-Processing-Systems-Conference/Neural-Information-Processing-Systems-Conference-NIPS-2016/Generative-Adversarial-Networks
http://wiseodd.github.io/techblog/2016/09/17/gan-tensorflow/
https://www.slideshare.net/ThomasDaSilvaPaula/a-very-gentle-introduction-to-generative-adversarial-networks-aka-gans-71614428
http://blog.evjang.com/2016/06/generative-adversarial-nets-in.html
https://medium.com/@awjuliani/generative-adversarial-networks-explained-with-a-classic-spongebob-squarepants-episode-54deab2fce39

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13.8 Generative Adversarial Networks (LIVE)

http://www.youtube.com/watch?v=0VPQHbMvGzg

We're going to build a GAN to generate some images using Tensorflow. This will help you grasp the architecture and intuition behind adversarial approaches to machine learning. We're building a Deep Convolutional GAN to generate MNIST digits.

Code for this video:
https://github.com/llSourcell/Generative_Adversarial_networks_LIVE/blob/master/EZGAN.ipynb

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More Learning resources:
http://guimperarnau.com/blog/2017/03/Fantastic-GANs-and-where-to-find-them
http://www.cs.toronto.edu/~dtarlow/pos14/talks/goodfellow.pdf
https://datawarrior.wordpress.com/2017/02/03/generative-adversarial-networks/
https://www.quora.com/What-are-Generative-Adversarial-Networks
http://nuit-blanche.blogspot.com/2017/01/nips-2016-tutorial-generative.html
http://www.paddlepaddle.org/develop/doc/tutorials/gan/index_en.html
http://gkalliatakis.com/blog/delving-deep-into-gans

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http://wizards.herokuapp.com/

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13.9 How to Convert Text to Images - Intro to Deep Learning #16

http://www.youtube.com/watch?v=gmvRStL_Dag

Generative Adversarial Networks are back! We'll use the cutting edge StackGAN architecture to let us generate images from text descriptions alone. This is pretty wild stuff and there is so much room for improvement. The possibilities are endless. I'll go through the architecture, code, and the implications of this technology for humanity.

Special shoutout to new Patrons Joshua Tobkin, Cameron Tofer, and Zarathustra Technologies. I'll add you guys to the credits next video.

Code for this video:
https://github.com/llSourcell/how_to_convert_text_to_images

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More learning resources:
https://www.youtube.com/watch?v=rAbhypxs1qQ
https://www.youtube.com/watch?v=93yaf_kE0Fg
https://arxiv.org/abs/1612.03242
http://cs.stanford.edu/people/karpathy/gan/
http://blog.evjang.com/2016/06/generative-adversarial-nets-in.html

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693
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13.10 Generative Adversarial Networks for Style Transfer (LIVE)

http://www.youtube.com/watch?v=MgdAe-T8obE

Generative Adversarial Nets are such a rich topic for exploration, we're going to build one that was released just 2 months ago called the "DiscoGAN" that lets us transfer the style between 2 datasets. And I'll be building this using Tensorflow.

Code for this video:
https://github.com/llSourcell/GANS-for-style-transfer

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More learning resources:
https://arxiv.org/abs/1703.05192
https://github.com/SKTBrain/DiscoGAN
https://www.reddit.com/r/MachineLearning/comments/5zp0eu/r_170305192_learning_to_discover_crossdomain/
https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7

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14 Music Generation

14.1 Build an AI Composer - Machine Learning for Hackers #2

http://www.youtube.com/watch?v=S_f2qV2_U00

This video will get you up and running with your first AI composer in just 10 lines of Python. The app can compose british folk songs after training on an existing folk dataset.

The code for this video is here:
https://github.com/llSourcell/AI_Composer

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

This is 'a' way to generate music, it's not necessarily the absolute best way. Another attempt I really like is this one since it can generate not just monophonic music, but polyphonic music as well:

http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/

Tensorflow install instructions here:
https://www.tensorflow.org/versions/r0.8/get_started/os_setup.html#pip-installation

In a future video, I'll discuss how to easily use cloud GPU computing. Likely using www.fomoro.com

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment.
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14.2 Generate Music in TensorFlow

http://www.youtube.com/watch?v=ZE7qWXX05T0

In this video, I go over some of the state of the art advances in music generation coming out of DeepMind. Then we build our own music generation script in Python using Tensorflow and a type of neural network called a Restricted Boltzmann Machine. Congrats to Rohan Verma (Winner) and Chih-Cheng Liang (runner-up) for their classifiers for scientists. The challenge for this video is to generate a happy/upbeat song using the RBM Script.

The code for this video is here:
https://github.com/llSourcell/Music_Generator_Demo

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

The WaveNet blogpost with audio samples:
https://deepmind.com/blog/wavenet-generative-model-raw-audio/

More on RBMs:
http://deeplearning4j.org/restrictedboltzmannmachine.html

Another write up on music generation with Neural Networks:
http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/

Interesting Machine Music Generation Project by Google:
https://magenta.tensorflow.org/welcome-to-magenta

TensorFlow course on Udacity:
https://www.udacity.com/course/deep-learning--ud730

Rohan's Classifier (Winner):
https://github.com/rhnvrm/galaxy-image-classifier-tensorflow

Chih-Cheng's Classifier (Runner-up):
https://github.com/ChihChengLiang/tensorflow-night-heron-classifier

Please subscribe, like, and comment! You guys are the reason I do this. Thanks so much for watching my videos! If you enjoy my videos, I'd appreciate your support on Patreon :)

https://www.patreon.com/user?u=3191693
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14.3 How to Generate Music - Intro to Deep Learning #9

http://www.youtube.com/watch?v=4DMm5Lhey1U

We're going to build a music generating neural network trained on jazz songs in Keras. I'll go over the history of algorithmic generation, then we'll walk step by step through the process of how LSTM networks help us generate music.

Coding Challenge for this video:
https://github.com/llSourcell/How-to-Generate-Music-Demo

Vishal's Winning Code:
https://github.com/erilyth/DeepLearning-SirajologyChallenges/tree/master/Art_Generation

Michael's Runner up code:
https://github.com/michalpelka/How-to-Generate-Art-Demo/blob/master/demo.ipynb

More Learning Resources:
https://medium.com/@shiyan/understanding-lstm-and-its-diagrams-37e2f46f1714#.swstv6z61
http://mourafiq.com/2016/05/15/predicting-sequences-using-rnn-in-tensorflow.html
https://magenta.tensorflow.org/2016/06/10/recurrent-neural-network-generation-tutorial/
http://deeplearning.net/tutorial/rnnrbm.html
https://maraoz.com/2016/02/02/abc-rnn/
http://www.cs.cmu.edu/~music//cmsip/slides/05-algo-comp.pdf
http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/
https://www.reddit.com/r/algorithmicmusic/

Please Subscribe! And like. And comment. That's what keeps me going.

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693

Thanks Ji-Sung Kim for the example code:
https://deepjazz.io
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14.4 How to Generate Music with Tensorflow (LIVE)

http://www.youtube.com/watch?v=pg9apmwf7og

This live session will focus on the details of music generation using the Tensorflow library. The goal is for you to understand the details of how to encode music, feed it to a well tuned model, and use it to generate really cool sounds. And I'm going to NOT use Google Hangouts, instead I'll do this with a green screen and a DSLR camera :)

Code for this video:
https://github.com/llSourcell/music_demo_live/

Please subscribe! And like. And comment. That's what keeps me going.

My Udacity course is open for enrollments until this Saturday at midnight:
https://www.udacity.com/course/deep-learning-nanodegree-foundation--nd101

More Learning Resources:
http://www.asimovinstitute.org/analyzing-deep-learning-tools-music/
http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/
https://github.com/hexahedria/biaxial-rnn-music-composition
http://www.hexahedria.com/2016/08/08/summer-research-on-the-hmc-intelligent-music-software-team
https://magenta.tensorflow.org/
https://github.com/farizrahman4u/seq2seq
http://stackoverflow.com/questions/14448380/how-do-i-read-a-midi-file-change-its-instrument-and-write-it-back
https://github.com/vishnubob/python-midi

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Please support me on Patreon:
https://www.patreon.com/user?u=3191693

Streaming Live from UploadVR's Studio in San Francisco!: https://www.youtube.com/uploadvr
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15 The Math of Intelligence

15.1 Intro - The Math of Intelligence

http://www.youtube.com/watch?v=xRJCOz3AfYY

Welcome to The Math of Intelligence! In this 3 month course, we'll cover the most fundamental math concepts in Machine Learning. In this first lesson, we'll go over a very popular optimization technique called gradient descent to help us predict how many calories a cyclist would burn given just their distance traveled. We'll also follow the story of 2 data scientists as they attempt to find the Higgs-Boson (God particle) via anomaly detection. No collaborations, this is an independent course.

Code for this video (with challenge details):
https://github.com/llSourcell/Intro_to_the_Math_of_intelligence

TypicalHog's winning code:
https://github.com/TypicalHog/THCrypt

Syllabus for this course:
https://github.com/llSourcell/The_Math_of_Intelligence

Please Subscribe! And like. And comment. That's what keeps me going.

More learning resources:
http://machinelearningmastery.com/linear-regression-tutorial-using-gradient-descent-for-machine-learning/
https://spin.atomicobject.com/2014/06/24/gradient-descent-linear-regression/
https://www.coursera.org/learn/machine-learning/lecture/kCvQc/gradient-descent-for-linear-regression
http://cs229.stanford.edu/notes/cs229-notes1.pdf
http://blog.hackerearth.com/gradient-descent-algorithm-linear-regression
https://www.r-bloggers.com/linear-regression-by-gradient-descent/
https://www.youtube.com/watch?v=XdM6ER7zTLk&t=1650s

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693
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15.2 Support Vector Machines - The Math of Intelligence (Week 1)

http://www.youtube.com/watch?v=g8D5YL6cOSE

Support Vector Machines are a very popular type of machine learning model used for classification when you have a small dataset. We'll go through when to use them, how they work, and build our own using numpy. This is part of Week 1 of The Math of Intelligence. This is a re-recorded version of a video I just released a day ago (the audio/video quality is better in this one)

Code for this video:
https://github.com/llSourcell/Classifying_Data_Using_a_Support_Vector_Machine

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Course Syllabus:
https://github.com/llSourcell/The_Math_of_Intelligence

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

More Learning resources:
https://www.analyticsvidhya.com/blog/2015/10/understaing-support-vector-machine-example-code/
http://www.robots.ox.ac.uk/~az/lectures/ml/lect2.pdf
http://machinelearningmastery.com/support-vector-machines-for-machine-learning/
http://www.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tutorial.pdf
http://www.statsoft.com/Textbook/Support-Vector-Machines
https://www.youtube.com/watch?v=_PwhiWxHK8o

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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15.3 Second Order Optimization - The Math of Intelligence #2

http://www.youtube.com/watch?v=UIFMLK2nj_w

Gradient Descent and its variants are very useful, but there exists an entire other class of optimization techniques that aren't as widely understood. We'll learn about second order method variants, how they compare to first order methods, and implement our own in Python.

Code for this video (with challenge):
https://github.com/llSourcell/Second_Order_Optimization_Newtons_Method

Alberto's Winning Code:
https://github.com/alberduris

Ivan's Runner up Code:
https://github.com/PiaFraus

Please Subscribe! And like. And comment. That's what keeps me going.

Course Syllabus:
https://github.com/llSourcell/The_Math_of_Intelligence

More learning resources:
https://web.stanford.edu/class/msande311/lecture13.pdf
https://www.cs.toronto.edu/~hinton/csc2515/notes/lec6tutorial.pdf
https://www.quora.com/In-mathematical-optimization-problems-the-first-derivative-is-often-used-Why-not-the-second-or-higher-order-derivatives
https://en.wikipedia.org/wiki/Newton%27s_method_in_optimization
https://www.youtube.com/watch?v=28BMpgxn_Ec&t=444s
https://www.youtube.com/watch?v=42zJ5xrdOqo&t=438s

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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15.4 Logistic Regression - The Math of Intelligence (Week 2)

http://www.youtube.com/watch?v=D8alok2P468

We're going to use logistic regression to predict if someone has diabetes or not given 3 body metrics! We'll use Newton's method to help us optimize the model. We'll use a bit from all the mathematical disciplines i've mentioned (calculus, linear algebra, probability theory, statistics).

Code for this video:
https://github.com/llSourcell/logistic_regression_newtons_method

Please subscribe! And like. And comment. That's what keeps me going.

More learning resources:
http://www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf
http://openclassroom.stanford.edu/MainFolder/DocumentPage.php?course=MachineLearning&doc=exercises/ex4/ex4.html
https://www.youtube.com/watch?v=X-7sA83PjPM
https://www.youtube.com/watch?v=TuttBDdbls8
https://rstudio-pubs-static.s3.amazonaws.com/160015_b192ca9855e84b57814e785ebd034a5e.html
https://www.r-bloggers.com/machine-learning-ex4-logistic-regression-and-newtons-method/
https://statacumen.com/teach/SC1/SC1_11_LogisticRegression.pdf

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15.5 Vectors - The Math of Intelligence #3

http://www.youtube.com/watch?v=s0Q3CojqRfM

We're going to explore why the concept of vectors is so important in machine learning. We'll talk about how they are used to represent both data and models. Get ready for some Linear Algebra!

Code for this video (with challenge):
https://github.com/llSourcell/Vectors_Linear_Algebra/tree/master

Vishnu's Winning Code:
https://github.com/Sri-Vishnu-Kumar-K/MathOfIntelligence/blob/master/second_order_optimization_newtons_method/second_order_optimization.py

Hammad's Runner-up Code:
https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/blob/master/Newtons%20Method.ipynb

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More learning resources:
http://mathworld.wolfram.com/VectorNorm.html
http://www.math.usm.edu/lambers/mat610/sum10/lecture2.pdf
https://www.youtube.com/watch?v=tXCqr2UsbWQ
https://stackoverflow.com/questions/38379905/what-is-vector-in-terms-of-machine-learning
http://www.chioka.in/differences-between-the-l1-norm-and-the-l2-norm-least-absolute-deviations-and-least-squares/
https://www.quora.com/What-is-the-difference-between-L1-and-L2-regularization

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15.6 K-Means Clustering - The Math of Intelligence (Week 3)

http://www.youtube.com/watch?v=9991JlKnFmk

Let's detect the intruder trying to break into our security system using a very popular ML technique called K-Means Clustering! This is an example of learning from data that has no labels (unsupervised) and we'll use some concepts that we've already learned about like computing the Euclidean distance and a loss function to do this.

Code for this video:
https://github.com/llSourcell/k_means_clustering

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More learning resources:
http://www.kdnuggets.com/2016/12/datascience-introduction-k-means-clustering-tutorial.html
http://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_ml/py_kmeans/py_kmeans_understanding/py_kmeans_understanding.html
http://people.revoledu.com/kardi/tutorial/kMean/
https://home.deib.polimi.it/matteucc/Clustering/tutorial_html/kmeans.html
http://mnemstudio.org/clustering-k-means-example-1.htm
https://www.dezyre.com/data-science-in-r-programming-tutorial/k-means-clustering-techniques-tutorial
http://scikit-learn.org/stable/tutorial/statistical_inference/unsupervised_learning.html

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15.7 Neural Networks - The Math of Intelligence #4

http://www.youtube.com/watch?v=ov_RkIJptwE

Have you ever wondered what the math behind neural networks looks like? What gives them such incredible power? We're going to cover 4 different neural networks in this video to develop an intuition around their basic principles (2 feedforward networks, 1 recurrent network, and a self-organizing map). Prepare yourself, deep learning is coming.

Code for this video (with coding challenge):
https://github.com/llSourcell/neural_networks

Hammad's winning code:
https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Regularization%20in%20Linear%20Regression

Ong's runner-up code:
https://github.com/jrios6/Math-of-Intelligence/tree/master/3-Regularization

More learning resources:
https://www.youtube.com/watch?v=h3l4qz76JhQ
http://www.ai-junkie.com/ann/som/som1.html
http://iamtrask.github.io/2015/07/12/basic-python-network/
https://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3

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15.8 Convolutional Neural Networks - The Math of Intelligence (Week 4)

http://www.youtube.com/watch?v=FTr3n7uBIuE

Convolutional Networks allow us to classify images, generate them, and can even be applied to other types of data. We're going to build one in numpy that can classify and type of alphanumeric character and it will run in a Flask web app.

Code for this video:
https://github.com/llSourcell/Convolutional_neural_network

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More learning resources:
https://github.com/dorajam/Convolutional-Network
https://beckernick.github.io/neural-network-scratch/
https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner%27s-Guide-To-Understanding-Convolutional-Neural-Networks/
http://cs231n.github.io/convolutional-networks/
http://deeplearning.net/tutorial/lenet.html
https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
https://www.youtube.com/watch?v=q555kfIFUCM&t=31s

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15.9 Dimensionality Reduction - The Math of Intelligence #5

http://www.youtube.com/watch?v=jPmV3j1dAv4

Most of the datasets you'll find will have more than 3 dimensions. How are you supposed to understand visualize n-dimensional data? Enter dimensionality reduction techniques. We'll go over the the math behind the most popular such technique called Principal Component Analysis.

Code for this video:
https://github.com/llSourcell/Dimensionality_Reduction

Ong's Winning Code:
https://github.com/jrios6/Math-of-Intelligence/tree/master/4-Self-Organizing-Maps

Hammad's Runner up Code:
https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Self%20Organizing%20Maps%20for%20Data%20Visualization

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I used a screengrab from 3blue1brown's awesome videos: https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw

More learning resources:
https://plot.ly/ipython-notebooks/principal-component-analysis/
https://www.youtube.com/watch?v=lrHboFMio7g
https://www.dezyre.com/data-science-in-python-tutorial/principal-component-analysis-tutorial
https://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/
http://setosa.io/ev/principal-component-analysis/
http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html
https://algobeans.com/2016/06/15/principal-component-analysis-tutorial/

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15.10 Recurrent Neural Network - The Math of Intelligence (Week 5)

http://www.youtube.com/watch?v=BwmddtPFWtA

Recurrent neural networks let us learn from sequential data (time series, music, audio, video frames, etc ). We're going to build one from scratch in numpy (including backpropagation) to generate a sequence of words in the style of Franz Kafka.

Code for this video:
https://github.com/llSourcell/recurrent_neural_network

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More learning resources:
https://www.youtube.com/watch?v=hWgGJeAvLws
https://www.youtube.com/watch?v=cdLUzrjnlr4
https://medium.freecodecamp.org/dive-into-deep-learning-with-these-23-online-courses-bf247d289cc0
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
https://deeplearning4j.org/lstm.html
http://karpathy.github.io/2015/05/21/rnn-effectiveness/

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http://wizards.herokuapp.com/

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15.11 Probability Theory - The Math of Intelligence #6

http://www.youtube.com/watch?v=PrkiRVcrxOs

We'll build a Spam Detector using a machine learning model called a Naive Bayes Classifier! This is our first real dip into probability theory in the series; I'll talk about the types of probability, then we'll use Bayes Theorem to help us build our classifier.

Code for this video:
https://github.com/llSourcell/naive_bayes_classifier/

Hammad's Winning Code:
https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Principal%20Component%20Analysis

Kristian's Runner up Code:
https://github.com/kwichmann/PCA_and_autoencoders

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More Learning Resources:
http://machinelearningmastery.com/naive-bayes-tutorial-for-machine-learning/
http://blog.datumbox.com/machine-learning-tutorial-the-naive-bayes-text-classifier/
http://machinelearningmastery.com/naive-bayes-classifier-scratch-python/
https://www.analyticsvidhya.com/blog/2015/09/naive-bayes-explained/
https://www.youtube.com/watch?v=psHrcSacU9Y
https://hackernoon.com/how-to-build-a-simple-spam-detecting-machine-learning-classifier-4471fe6b816e
https://www.autonlab.org/tutorials/naive.html

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15.12 Random Forests - The Math of Intelligence (Week 6)

http://www.youtube.com/watch?v=QHOazyP-YlM

This is one of the most used machine learning models ever. Random Forests can be used for both regression and classification, and our use case will be to assess whether someone is credible or not by analyzing their financial history!

DL nanodegree open for another round! we'll pick one random student that signs up in next 24 hrs to collab w/ me one-on-one on a DL music project
https://www.udacity.com/course/deep-learning-nanodegree-foundation--nd101

Code for this video:
https://github.com/llSourcell/random_forests

Please Subscribe! And like. And comment. That's what keeps me going.

More learning resources:
https://ujjwalkarn.me/2016/05/30/a-curated-list-of-python-tutorials-for-data-science-nlp-and-machine-learning/
https://www.coursera.org/learn/machine-learning-data-analysis/lecture/eTO92/building-a-random-forest-with-python
https://github.com/kevin-keraudren/randomforest-python
http://kldavenport.com/pure-python-decision-trees/
http://blog.yhat.com/posts/random-forests-in-python.html
https://www.analyticsvidhya.com/blog/2016/04/complete-tutorial-tree-based-modeling-scratch-in-python/
http://machinelearningmastery.com/implement-decision-tree-algorithm-scratch-python/
http://machinelearningmastery.com/implement-random-forest-scratch-python/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693
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15.13 Hyperparameter Optimization - The Math of Intelligence #7

http://www.youtube.com/watch?v=ttE0F7fghfk

Hyperparameters are the magic numbers of machine learning. We're going to learn how to find them in a more intelligent way than just trial-and-error. We'll go over grid search, random search, and Bayesian Optimization. I'll also cover the difference between Bayesian and Frequentist probability.

Code for this video: https://github.com/llSourcell/hyperparameter_optimization_strategies

Noah's Winning Code:
https://github.com/NoahLidell/math-of-intelligence/tree/master/probability_theory

Hammad's Runner-up Code:
https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Naive%20Bayes%20Classifier

More learning resources:
https://www.iro.umontreal.ca/~bengioy/cifar/NCAP2014-summerschool/slides/Ryan_adams_140814_bayesopt_ncap.pdf
https://thuijskens.github.io/2016/12/29/bayesian-optimisation/
https://jmhessel.github.io/Bayesian-Optimization/
https://arimo.com/data-science/2016/bayesian-optimization-hyperparameter-tuning/
https://dhnzl.files.wordpress.com/2016/12/fuzzymad2016_bo_pdf.pdf
http://blog.revolutionanalytics.com/2016/06/bayesian-optimization-of-machine-learning-models.html
https://www.youtube.com/watch?v=cWQDeB9WqvU
https://nlpers.blogspot.nl/2014/10/hyperparameter-search-bayesian.html
http://neupy.com/2016/12/17/hyperparameter_optimization_for_neural_networks.html

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693

Thanks to Veritasium (bayesian animation) & Angela Schoellig (drone clip)
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15.14 Gaussian Mixture Models - The Math of Intelligence (Week 7)

http://www.youtube.com/watch?v=JNlEIEwe-Cg

We're going to predict customer churn using a clustering technique called the Gaussian Mixture Model! This is a probability distribution that consists of multiple Gaussian distributions, very cool. I also have something important but unrelated to say in the beginning of the video.

Code for this video:
https://github.com/llSourcell/Gaussian_Mixture_Models

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More learning resources:
http://yulearning.blogspot.nl/2014/11/einsteins-most-famous-equation-is-emc2.html
http://web.iitd.ac.in/~sumeet/GMM_said_crv10_tutorial.pdf
https://brilliant.org/wiki/gaussian-mixture-model/
http://www.vlfeat.org/overview/gmm.html
http://www.informatica.uniroma2.it/upload/2009/IM/mixture-tutorial.pdf
http://cs.nyu.edu/~dsontag/courses/ml12/slides/lecture21.pdf
http://statweb.stanford.edu/~tibs/stat315a/LECTURES/em.pdf

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15.15 Generative Models - The Math of Intelligence #8

http://www.youtube.com/watch?v=HyuBTMaKFmU

Generative Models are insanely cool! They help create never before seen data. We'll go over the mathematical difference between discriminative and generative models, talk about a few types, then dive into a basic one called Latent Dirichlet Allocation to generate a set of topics for some news articles.

Code for this video:
https://github.com/llSourcell/Latent_Dirichlet_Allocation

Hammad's Winning Code:
https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Bayesian%20Linear%20Regression

Noah's Runner up code:
https://github.com/NoahLidell/math-of-intelligence/tree/master/hyperparameter_optimization

Carykh's channel:
https://www.youtube.com/user/carykh

More learning resources:
https://www.youtube.com/watch?v=qCA1Dk_Ih_c&t=383s
http://blog.echen.me/2011/08/22/introduction-to-latent-dirichlet-allocation/
http://ai.stanford.edu/~ang/papers/jair03-lda.pdf
https://rstudio-pubs-static.s3.amazonaws.com/79360_850b2a69980c4488b1db95987a24867a.html
https://www.quora.com/What-is-a-good-explanation-of-Latent-Dirichlet-Allocation
https://blog.bigml.com/2016/11/16/introduction-to-topic-models/
https://www.analyticsvidhya.com/blog/2016/08/beginners-guide-to-topic-modeling-in-python/
https://github.com/blei-lab/onlineldavb

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https://www.patreon.com/user?u=3191693
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15.16 LSTM Networks - The Math of Intelligence (Week 8)

http://www.youtube.com/watch?v=9zhrxE5PQgY

Recurrent Networks can be improved to remember long range dependencies by using whats called a Long-Short Term Memory (LSTM) Cell. Let's build one using just numpy! I'll go over the cell components as well as the forward and backward pass logic.

Code for this video:
https://github.com/llSourcell/LSTM_Networks

Please Subscribe! And like. And comment. Thats what keeps me going.

More learning resources:
https://www.youtube.com/watch?v=ftMq5ps503w
https://www.youtube.com/watch?v=cdLUzrjnlr4
https://www.youtube.com/watch?v=hWgGJeAvLws
http://www.wildml.com/2015/09/recurrent-neural-networks-tutorial-part-1-introduction-to-rnns/
https://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/

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15.17 Deep Q Learning for Video Games - The Math of Intelligence #9

http://www.youtube.com/watch?v=79pmNdyxEGo

We're going to replicate DeepMind's Deep Q Learning algorithm for Super Mario Bros! This bot will be able to play a bunch of different video games by using reinforcement learning. This is the first video in this series that uses libraries (Keras & Gym) because if it didn't, the code would be way too long for a short video. I'll make a longer, in-depth version without libraries soon.

Code for this video:
https://github.com/llSourcell/deep_q_learning

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More learning resources:
https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0
http://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html
http://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/
http://karpathy.github.io/2016/05/31/rl/
https://yanpanlau.github.io/2016/07/10/FlappyBird-Keras.html
https://keon.io/deep-q-learning/
http://www0.cs.ucl.ac.uk/staff/d.silver/web/Resources_files/deep_rl.pdf
http://mnemstudio.org/path-finding-q-learning-tutorial.htm

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15.18 Genetic Algorithm in Artificial Intelligence - The Math of Intelligence (Week 9)

http://www.youtube.com/watch?v=rGWBo0JGf50

Evolutionary/genetic algorithms are somewhat of a mystery to many in the machine learning discipline. You don't see papers regularly published using them but they are a really fascinating subfield and in this video, we're going to use a genetic algorithm to improve invaders in a space invaders game!

Code for this video:
https://github.com/llSourcell/Evolutionary_Space_Invaders

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More learning resources:
http://www.ai-junkie.com/ga/intro/gat1.html
http://www.tutorialspoint.com/genetic_algorithms/
http://www.theprojectspot.com/tutorial-post/creating-a-genetic-algorithm-for-beginners/3
http://www.obitko.com/tutorials/genetic-algorithms/
http://www-cs-students.stanford.edu/~jl/Essays/ga.html
http://www.alanzucconi.com/2016/04/06/evolutionary-coputation-1/

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15.19 Quantum Algorithm - The Math of Intelligence #10

http://www.youtube.com/watch?v=LhtnECml-KI

Quantum Computing offers hope for computing progress as we approach the limits of transistor density on silicon hardware. We're going to talk about the theory behind them then build our own quantum algorithm using IBM's Quantum API! This is the last episode of this series.

Code for this video:
https://github.com/llSourcell/quantum_computing

Noah's Winning code:
https://github.com/NoahLidell/math-of-intelligence/tree/master/q_learning

jhGitHub009's Runner Up code:
https://github.com/jhGitHub009/Game_bot_DQN

Please Subscribe! And like. And comment. That's what keeps me going.

More learning resources:
https://people.cs.umass.edu/~strubell/doc/quantum_tutorial.pdf
https://physics.stackexchange.com/questions/3390/can-anybody-provide-a-simple-example-of-a-quantum-computer-algorithm
http://michaelnielsen.org/blog/quantum-computing-for-everyone/
https://www.dwavesys.com/tutorials/background-reading-series/quantum-computing-primer
http://www.quantumplayground.net/#/home
https://www.research.ibm.com/ibm-q/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693

Special thanks to TED & Kurzgesagt for the animation clips
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16 Succeeding as a Programmer

16.1 How to Make Money as a Programmer in 2018

http://www.youtube.com/watch?v=BMT7FMwOIKc

I'll go through 5 methods that you can use to make money as a programmer! We are lucky in that our skill will only get more valuable to society over time. Links to everything I've discussed are below.

Please Subscribe! And like. And comment. That's what keeps me going.

Contract work:
http://upwork.com/
http://freelancer.com/
https://github.com/engineerapart/TheRemoteFreelancer (huge list)

Improving your portfolio:
http://www.codeofhonor.com/blog/marketing-yourself-as-a-programmer
https://softwareengineering.stackexchange.com/questions/54506/how-to-market-yourself-as-a-software-developer/59875

Paul Graham's goldmine of essays on starting a startup:
http://www.paulgraham.com/articles.html

Programming Challenges:
http://topcoder.com/
https://www.hackerearth.com/
https://www.codechef.com/

Bounty listings:
https://www.bountysource.com/
https://bountify.co/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Follow me:
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16.2 How to Succeed in any Programming Interview 2018

http://www.youtube.com/watch?v=5KB5KAak6tM

I'll show you the 5 steps to succeed in any technical interview. We'll go over what a great study plan looks like, resources to help you find jobs, and how you should conduct yourself during the interview.

Please Subscribe! That is the one thing you could do that would make me happiest.

Links from the video below

My Code School (Intro to Data Structures):
https://www.youtube.com/watch?v=92S4zgXN17o&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P&index=1

MIT Open Courseware (Intro to Algorithms):
https://www.youtube.com/watch?v=HtSuA80QTyo&index=1&list=PLSX2U_ZE4Huk19DPn34oZlygPbsig380X

HackerEarth and HackerRank:
https://www.hackerearth.com/
https://www.hackerrank.com/

Programming Interview Exposed:
http://books.lihui.org/cs2/Wiley%20-%20Programming%20Interviews%20Exposed_Secrets%20to%20Landing%20Your%20Next%20Job%20(2000).pdf

Cracking the Coding Interview:
https://github.com/yuanhui-yang/Cracking-the-Coding-Interview/blob/master/Cracking%20the%20Coding%20Interview%20-%204th%20Edition.pdf

How to Conduct a Mock Interview:
http://web.stanford.edu/dept/CTL/Oralcomm/Microsoft%20Word%20-%20How%20to%20Conduct%20Mock%20Interviews.pdf

Angellist:
https://angel.co/

HackerNews Who's Hiring:
https://news.ycombinator.com/item?id=13541679

Making a great resume:
https://medium.com/@order_group/job-interview-and-good-resume-cv-tips-for-programmers-from-our-experts-3aa626c825ab#.ssdw5a2th

Passing the Interview:
http://blog.triplebyte.com/how-to-pass-a-programming-interview

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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16.3 How to Learn Advanced Concepts Fast

http://www.youtube.com/watch?v=nxWfZP6eslM

These are 10 strategies I use to learn advanced concepts as fast as possible! I'm going to explain each one, and give some examples of what I mean. No coding challenge this week, hope its helpful!

10 Strategies below:

1. Find a reason to learn
2. Start with the simplest explanations
3. Create a set of small, achievable goals
4. Set Deadlines
5. Maintain a flow state
6. Let your curiosity guide your learning path
7. Spend 1/3 of your time researching & 2/3 doing
8. Take notes by hand
9. Dont multitask
10. Maintain your Health

(yes, i mis-numbered some of them in the video accidentally)

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16.4 How to Start an AI Startup

http://www.youtube.com/watch?v=9bbS-trc8ys

How are you supposed to get in on the AI hype? Deep learning has enabled a whole new breed of applications, and there are still so many different opportunities to apply it in fields that are completely untapped. I'll go through the steps you need to take to start your own AI startup using a combination of my own experiences and best practices from the industry as a guide. From data collection to model training to picking a problem, we'll try to understand this challenging task.

Please Subscribe! And like. And comment. That's what keeps me going.

Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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instagram: https://www.instagram.com/sirajraval

Sources:
https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A/playlists
https://www.deeplearning.ai/
http://www.fast.ai/
http://www.deeplearningbook.org/
https://www.kaggle.com/datasets
https://github.com/awesomedata/awesome-public-datasets
https://archive.ics.uci.edu/ml/datasets.html

More learning resources:
https://www.youtube.com/watch?v=CBYhVcO4WgI
https://www.youtube.com/watch?v=bNpx7gpSqbY
https://www.youtube.com/watch?v=JqxzLUE6pP8
https://www.youtube.com/watch?v=ii1jcLg-eIQ
https://www.youtube.com/watch?v=ia8arCDoxZ8
https://www.youtube.com/watch?v=677ZtSMr4-4

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Signup for my newsletter for exciting updates in the field of AI:
https://goo.gl/FZzJ5w

16.5 How to Overcome Failure

http://www.youtube.com/watch?v=kOLSDsjeSIE

Everyone fails. In this video, i'll recount 5 times in my life where I failed and talk about how I recovered. Whether it be in work life, school life, or personal life, failure is just a reality of life. Its how you deal with it that defines your future. If you're wondering, I recorded this is Lisbon Portugal since I was invited to speak at a Data Science meetup about blockchain AI. I took this is my airbnb when i had some free time.

Hammad's Winning Code:
https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/blob/master/Binary%20Logistic%20Regression/Binary%20Logistic%20Regression.ipynb

Wladi's Runner up code:
https://github.com/wladiarce/logistic_regression_numpy

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Sign up for the next course at The School of AI:
http://theschool.ai/

More learning resources:
https://simpleprogrammer.com/overcoming-obstacles-stoic-mindset/
https://blog.todoist.com/2015/04/14/overcome-fear-of-failure/
https://www.quora.com/How-can-I-overcome-the-fear-of-failure-especially-fear-of-coding
https://thenextweb.com/dd/2015/06/11/8-barriers-to-overcome-when-learning-to-code/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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16.6 10 People that Inspire Me

http://www.youtube.com/watch?v=fxzw5vychsg

2 days left to enroll in my new course, learn more and signup here -
https://www.theschool.ai/courses/decentralized-applications

I'm going to list 10 people that inspire me so that hopefully you get some inspiration from them as well. These people are AI researchers, blockchain researchers, artists, and CEOs. This list is in no particular order, I'll list all of their social profiles below so you can follow them. Lets get started!

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#1 Naval Ravikant
https://twitter.com/naval
Mind-blowing interview: https://www.youtube.com/watch?v=IrSn3zx2GbM&t=468s

#2 Oriol Vinyals
https://twitter.com/OriolVinyalsML

#3 Andrew Trask
https://twitter.com/iamtrask
My interview with Andrew: https://www.youtube.com/watch?v=qJ1rdVEcl5g&t=340s

#4 Balaji Srinivasan
https://twitter.com/balajis
On Exit: https://www.youtube.com/watch?v=cOubCHLXT6A&t=572s

#5 Bryan Johnson
https://twitter.com/bryan_johnson
interview: https://www.youtube.com/watch?v=L3t8-8Z5w5U

#6 Tristan Harris
https://twitter.com/tristanharris
Amazing podcast: https://www.youtube.com/watch?v=jlPF9_1VIso

#7 Tupac Shakur
https://www.youtube.com/watch?v=GL-ZoNhUFmc

#8 Juan Benet
https://www.youtube.com/watch?v=iUVLuXjPAfg&t=5724s

#9 Steve Jobs
My fav video: https://www.youtube.com/watch?v=keCwRdbwNQY&t=275s

#10 Trent McConaughy
https://www.youtube.com/watch?v=P1txT3kdJRE
Signup for my newsletter for exciting updates in the field of AI:
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16.7 How to Get an AI Internship

http://www.youtube.com/watch?v=CGTn0ceOaOM

This is a question I get asked a lot, so I've decided to make a video detailing how to get an AI internship. Internships are a great way to start a career in AI! They enable you to build a professional network and can be amazing learning experiences. I'll list a ton of resources and discuss the most helpful steps in the process including creating a study plan, finding a relevant position, building a personal brand, leveraging your existing network, and practicing for interviews. Enjoy!

Please Subscribe! And like. And comment. That's what keeps me going.

Want more education? Connect with me here:
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instagram: https://www.instagram.com/sirajraval

The School of AI:
https://www.theschool.ai

I wasn't kidding about the million dollar salaries bit:
https://www.nytimes.com/2018/04/19/technology/artificial-intelligence-salaries-openai.html

Who to Follow in AI:
https://medium.com/@alexrachnog/ultimate-following-list-to-keep-updated-in-artificial-intelligence-32776ffcd079

Learn Machine Learning in 3 Months:
https://www.youtube.com/watch?v=Cr6VqTRO1v0

How to Learn Math Fast:
https://www.youtube.com/watch?v=YzfdL58virc&vl=en

Job listings:
https://intern.supply/
https://www.angel.co

Project Ideas:
https://github.com/NirantK/awesome-project-ideas
https://github.com/llSourcell

How to Read Research Papers:
https://www.youtube.com/watch?v=SHTOI0KtZnU&t=42s

How to Write Research Papers:
https://www.youtube.com/watch?v=S47RIVkr978

How to Create a Great AI Resume:
https://www.youtube.com/watch?v=nMK94JlKRb4

How to Succeed in any Programming Interview:
https://www.youtube.com/watch?v=5KB5KAak6tM&t=102s

Join us in the Wizards Slack channel (join #internships):
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693

#SirajRaval

Signup for my newsletter for exciting updates in the field of AI:
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Hiring? Need a Job? See our job board!:
www.theschool.ai/jobs/

Need help on a project? See our consulting group:
www.theschool.ai/consulting-group/

16.8 5 Steps to Pass Data Science Interviews

http://www.youtube.com/watch?v=OHhoLhYW2cg

Data Science is becoming more and more popular as a career choice since it offers both lucrative salaries and the opportunity to have high impact. The Data Science interview process is challenging, but with dedicated practice you can succeed. In this video, I'll outline the 7 steps to pass any Data Science Interview. We'll go over topics like studying techniques, portfolio optimization, and interviewing tips, all of which are prominent in the modern Data Science interview pipeline. I've listed all of the resources I've mentioned both in the video description and in the associated GitHub readme. Enjoy!

Plan for this video:
https://github.com/llSourcell/Data_Science_Interview_Guide

Please Subscribe! And Like. And comment. Thats what keeps me going.

Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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Step 1 - Ask yourself "Why"

Investor Insights for Inspiration
https://thesyndicate.vc/the-top-5-startup-technology-angel-investing-and-venture-capital-podcasts-in-order/

Investors to follow on Twitter
http://www.venturearchetypes.com/faq/Investors-On-Twitter-List.html

Data Stories Podcasts
http://datastori.es/

Step 2 - Create and Execute a Study Plan

Learn Data Science in 3 Months
https://www.youtube.com/watch?v=9rDhY1P3YLA

Chromebook Data Science
https://leanpub.com/universities/set/jhu/chromebook-data-science

Open Source University
https://github.com/ossu/data-science

Practice Technical Interviews
http://interviewing.io

Use Flashcards
https://www.brainscape.com/subjects/data-science

Find a Mentor
https://www.sharpestminds.com

Step 3 - Build a Portfolio

Design a resume + personal website
https://www.youtube.com/watch?v=nMK94JlKRb4

And have 3 projects on Github, one should have a web presence

Step 4 - Start Pitching for Jobs

- https://www.angel.co
- Ask friends on Social Media
- https://www.ventureloop.com/ventureloop/job_search.php (VC Portfolio companies job listings)
- https://news.ycombinator.com/
- Find recruiters using
"site:linkedin.com quora technical recruiter"
but replace quora with your company

Scheduling tool
https://calendly.com/sirajraval/

Step 5 - Complete the Interview

Study Data Science Interview Questions on Glassdoor
https://www.glassdoor.com/Interview/data-scientist-interview-questions-SRCH_KO0,14.htm

----------------

Join us at the School of AI:
https://theschool.ai/

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#DataScienceInterview #SirajRaval #MachineLearning

17 Intro to Deep Learning (Udacity Nanodegree)

17.1 How to Make a Prediction - Intro to Deep Learning #1

http://www.youtube.com/watch?v=vOppzHpvTiQ

Welcome to Intro to Deep Learning! This course is for anyone who wants to become a deep learning engineer. I'll take you from the very basics of deep learning to the bleeding edge over the course of 4 months. In this video, we’ll predict an animal’s body weight given it’s brain weight using linear regression via 10 lines of Python. I’ll have a live session every Wednesday at 10 AM PST that covers my weekly video topics in depth. You can click on the little notification bell next to the subscribe button to get an email notification whenever I’m live. And each session is recorded and uploaded to this channel in case you miss it. This Youtube content is 100% created by me (from the writing to the editing, etc.) , it’ll all be released on my channel, and it’s totally free.

I am also very proud and excited to announce my new, exclusive partnership with Udacity. Together, we’re offering the new Deep Learning Nanodegree Foundation program. If you want to take your game to the next level, this is for you! Especially since Udacity will be providing guaranteed admission to their groundbreaking Artificial Intelligence and Self-Driving Car Nanodegree programs to all graduates. They’re offering discounted limited-time pricing, so enroll now to enjoy the unique projects, program sets, and expert reviews. Plus, their community is amazing, so don’t forget to join the Slack channel after you enroll (I’ll be in there too!) And hey, I’m getting paid a small royalty from each enrollment, so let’s do this together!

Link to the Udacity nanodegree:
https://www.udacity.com/course/deep-learning-nanodegree-foundation--nd101

Please Subscribe! And like. And comment. That’s what keeps me going.

This weeks coding challenge (these weekly challenges are not related to the Udacity nanodegree projects, those are additional):
https://github.com/llSourcell/linear_regression_demo

Mick’s winning code:
https://github.com/mickvanhulst/q_learning

Vishal’s runner-up code:
https://github.com/erilyth/Q-Learning-on-Mazes

More learning resources:
http://machinelearningmastery.com/a-tour-of-machine-learning-algorithms/
https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice
https://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer
https://onlinecourses.science.psu.edu/stat501/node/250
http://machinelearningmastery.com/simple-linear-regression-tutorial-for-machine-learning/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

The vast majority of this course will use Tensorflow. It's just this first example that's using scikit-learn.

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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17.2 How to Do Linear Regression using Gradient Descent

http://www.youtube.com/watch?v=XdM6ER7zTLk

The point of this is to demonstrate the concept of gradient descent. Gradient descent is the most popular optimization strategy in deep learning, in particular an implementation of it called backpropagation. We are using gradient descent as our optimization strategy for linear regression. We'll draw the line of best fit to measure the relationship between student test scores and the amount of hours studied.

Code for this video:
https://github.com/llSourcell/linear_regression_live

Yes, I've done this video before. But I'm doing it again because
1. Gradient Descent is really important. Know how it works.
2. Last time was in Google Hangouts (Ghetto) this is better quality

More learning resources:
https://spin.atomicobject.com/2014/06/24/gradient-descent-linear-regression/
https://en.wikipedia.org/wiki/Gradient_descent
http://machinelearningmastery.com/gradient-descent-for-machine-learning/
https://www.analyticsvidhya.com/blog/2017/03/introduction-to-gradient-descent-algorithm-along-its-variants/
http://ufldl.stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent/

Join us in the Wizards Slack Channel:
http://wizards.herokuapp.com/

Please Subscribe! And like. And comment. That's what keeps me going.

And please support me on Patreon:
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17.3 How to Make a Neural Network - Intro to Deep Learning #2

http://www.youtube.com/watch?v=p69khggr1Jo

How do we learn? In this video, I'll discuss our brain's biological neural network, then we'll talk about how an artificial neural network works. We'll create our own single layer feedforward network in Python, demo it, and analyze the implications of our results. This is the 2nd weekly video in my intro to deep learning series (Udacity nanodegree)

The coding challenge for this video:
https://github.com/llSourcell/Make_a_neural_network

Ludo's winning code:
https://github.com/ludobouan/linear-regression-sklearn

Amanullah's runner up code:
https://github.com/amanullahtariq/MLAlgorithm/tree/eca367287f7874e08a790ce0b0c21567e0b38a22/Challenge/LinearRegression

Please subscribe! And like. And comment. That's what keeps me going.

More learning resources:
https://www.mcb80x.org/
http://cogsci.stackexchange.com/questions/7880/what-is-the-difference-between-biological-and-artificial-neural-networks
https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1#.fn92gnrar
http://natureofcode.com/book/chapter-10-neural-networks/
https://blog.dbrgn.ch/2013/3/26/perceptrons-in-python/
http://neuralnetworksanddeeplearning.com/chap2.html
https://iamtrask.github.io/2015/07/27/python-network-part2/

The guy at the beginning is my Jeet Kune Do instructor (Sifu Tim). Send him an email at sifutimr@gmail.com if you thought he was cool in the video. He would absolutely love it. Special thanks Catherine Olsson of OpenAI for being the hook to my backpropagation rap.

Please support me on Patreon:
https://www.patreon.com/user?u=3191693
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17.4 How to Use Tensorflow for Classification (LIVE)

http://www.youtube.com/watch?v=4urPuRoT1sE

In this live session I'll introduce & give an overview of Google's Deep Learning library, Tensorflow. Then we'll use it to build a neural network capable of predicting housing prices, with me explaining every step along the way.

Code for this video:
https://github.com/llSourcell/How_to_use_Tensorflow_for_classification-LIVE

Please subscribe! And like. And comment. That's what keeps me going.

More learning resources:
https://jalammar.github.io/visual-interactive-guide-basics-neural-networks/
https://www.oreilly.com/learning/hello-tensorflow
https://www.tensorflow.org/tutorials/mnist/beginners/
https://github.com/aymericdamien/TensorFlow-Examples
https://www.youtube.com/watch?v=2FmcHiLCwTU&t=84s
https://cs224d.stanford.edu/lectures/CS224d-Lecture7.pdf

Join other Wizards on our Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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17.5 How to Do Sentiment Analysis - Intro to Deep Learning #3

http://www.youtube.com/watch?v=si8zZHkufRY

In this video, we'll use machine learning to help classify emotions! The example we'll use is classifying a movie review as either positive or negative via TF Learn in 20 lines of Python.

Coding Challenge for this video:
https://github.com/llSourcell/How_to_do_Sentiment_Analysis

Ludo's winning code:
https://github.com/ludobouan/pure-numpy-feedfowardNN

See Jie Xun's runner up code:
https://github.com/jiexunsee/Neural-Network-with-Python

Tutorial on setting up an AMI using AWS:
http://www.bitfusion.io/2016/05/09/easy-tensorflow-model-training-aws/

More learning resources:
http://deeplearning.net/tutorial/lstm.html
https://www.quora.com/How-is-deep-learning-used-in-sentiment-analysis
https://gab41.lab41.org/deep-learning-sentiment-one-character-at-a-t-i-m-e-6cd96e4f780d#.nme2qmtll
http://k8si.github.io/2016/01/28/lstm-networks-for-sentiment-analysis-on-tweets.html
https://www.kaggle.com/c/word2vec-nlp-tutorial

Please Subscribe! And like. And comment. That's what keeps me going.

Join us in our Slack channel:
wizards.herokuapp.com

If you're wondering, I used style transfer via machine learning to add the fire effect to myself during the rap part.

Please support me on Patreon:
https://www.patreon.com/user?u=3191693
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17.6 Word2Vec (tutorial)

http://www.youtube.com/watch?v=pY9EwZ02sXU

In this video, we'll use a Game of Thrones dataset to create word vectors. Then we'll map these word vectors out on a graph and use them to tell us related words that we input. We'll learn how to process a dataset from scratch, go over the word vectorization process, and visualization techniques all in one session.

Code for this video:
https://github.com/llSourcell/word_vectors_game_of_thrones-LIVE

Join us in our Slack channel:
http://wizards.herokuapp.com/

More learning resources:
https://www.tensorflow.org/tutorials/word2vec/
https://radimrehurek.com/gensim/models/word2vec.html
https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words
http://sebastianruder.com/word-embeddings-1/
http://natureofcode.com/book/chapter-1-vectors/

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17.7 How to Do Mathematics Easily - Intro to Deep Learning #4

http://www.youtube.com/watch?v=N4gDikiec8E

Let's learn about some key math concepts behind deep learning shall we? We'll build a 3 layer neural network and dive into some key concepts that makes deep learning give us such incredible results.

Coding challenge for this video:
https://github.com/llSourcell/how_to_do_math_for_deep_learning

Jovian's Winning Code:
https://github.com/jovianlin/siraj-intro-to-DL-03/blob/master/Siraj%2003%20Challenge.ipynb

Vishal's Runner up Code:
https://github.com/erilyth/DeepLearning-SirajologyChallenges/tree/master/Sentiment_Analysis

Linear Algebra cheatsheet:
http://www.souravsengupta.com/cds2016/lectures/Savov_Notes.pdf

Calculus cheatsheet:
http://tutorial.math.lamar.edu/pdf/Calculus_Cheat_Sheet_All.pdf

Statistics cheatsheet:
http://web.mit.edu/~csvoss/Public/usabo/stats_handout.pdf

And if you have never had experience with any of these 3 and want to learn from absolute scratch, I'd recommend the respective KhanAcademy courses:
https://www.khanacademy.org/math

More Learning Resources:
https://people.ucsc.edu/~praman1/static/pub/math-for-ml.pdf
http://www.vision.jhu.edu/tutorials/ICCV15-Tutorial-Math-Deep-Learning-Intro-Rene-Joan.pdf
http://datascience.ibm.com/blog/the-mathematics-of-machine-learning/

Join us in our Slack channel:
http://wizards.herokuapp.com/

And Part I of this book is so dope, seriously:
http://www.deeplearningbook.org/

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17.8 How to Predict Music You Love (LIVE)

http://www.youtube.com/watch?v=18adykNGhHU

In this video, we're going to look at several different type of recommender systems in an iPython notebook. Popularity based, item-item collaborative, then user-item collaborative. Then we'll touch on the bleeding edge in deep learning at the end. Also I freestyle. Twice lol.

Code for this video:
https://github.com/llSourcell/recommender_live

More learning resources:
http://tech.hulu.com/blog/2016/08/01/cfnade.html
https://blogs.msdn.microsoft.com/carlnol/2012/06/23/co-occurrence-approach-to-an-item-based-recommender/
https://www.mapr.com/blog/inside-look-at-components-of-recommendation-engine
https://www.ics.uci.edu/~welling/teaching/CS77Bwinter12/presentations/course_Ricci/13-Item-to-Item-Matrix-CF.pdf
https://www.analyticsvidhya.com/blog/2016/06/quick-guide-build-recommendation-engine-python/
http://blogs.gartner.com/martin-kihn/how-to-build-a-recommender-system-in-python/

Join us in our Slack channel:
http://wizards.herokuapp.com/

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17.9 How to Make Data Amazing - Intro to Deep Learning #5

http://www.youtube.com/watch?v=koiTTim4M-s

In this video, we'll go through data preprocessing steps for 3 different datasets. We'll also go in depth on a dimensionality reduction technique called Principal Component Analysis.

Coding challenge for this video:
https://github.com/llSourcell/How_to_Make_Data_Amazing

Charles-David's Winning Code:
https://github.com/alkaya/earthquake-cotw

Siby Jack Grove's Runner-up code:
https://github.com/sibyjackgrove/Earthquake_predict/blob/master/earthquake_predict.ipynb

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More Learning Resources:
http://www.cs.ccsu.edu/~markov/ccsu_courses/datamining-3.html
http://www.slideshare.net/jasonrodrigues/data-preprocessing-5609305
http://iasri.res.in/ebook/win_school_aa/notes/Data_Preprocessing.pdf
http://staffwww.itn.liu.se/~aidvi/courses/06/dm/lectures/lec2.pdf
http://ufldl.stanford.edu/wiki/index.php/Data_Preprocessing
http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/
https://plot.ly/ipython-notebooks/principal-component-analysis/

Public datasets:
https://github.com/caesar0301/awesome-public-datasets
https://aws.amazon.com/public-datasets/
http://archive.ics.uci.edu/ml/index.html
https://dreamtolearn.com/ryan/1001_datasets

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17.10 How to Simplify Your Dataset Easily (LIVE)

http://www.youtube.com/watch?v=K796Ae4gLlY

We're going to compare some different techniques that reduce the dimensionality of our data so we can visualize it. We'll go through each one step by step including the math and I'll answer questions along the way. And I freestyle.

Code for this video:
https://github.com/llSourcell/How_to_Simplify_Your_Data-LIVE-

Links from the video:
https://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/
http://setosa.io/ev/eigenvectors-and-eigenvalues/

More learning resources:
https://plot.ly/ipython-notebooks/principal-component-analysis/
http://sebastianraschka.com/Articles/2014_pca_step_by_step.html
https://www.quora.com/What-is-the-difference-between-LDA-and-PCA-for-dimension-reduction
https://www.quora.com/What-advantages-the-t-sne-algorithm-has-over-pca
http://stats.stackexchange.com/questions/123040/whats-wrong-with-t-sne-vs-pca-for-dimensional-reduction-using-r
https://www.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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17.11 How to Make an Image Classifier - Intro to Deep Learning #6

http://www.youtube.com/watch?v=cAICT4Al5Ow

We're going to make our own Image Classifier for cats & dogs in 40 lines of Python! First we'll go over the history of image classification, then we'll dive into the concepts behind convolutional networks and why they are so amazing.

Coding challenge for this video:
https://github.com/llSourcell/how_to_make_an_image_classifier

Charles-David's winning code:
https://github.com/alkaya/TFmyValentine-cotw

Dalai's runner-up code:
https://github.com/mdalai/Deep-Learning-projects/tree/master/wk5-speed-dating

More Learning Resources:
http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/
https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/
http://cs231n.github.io/convolutional-networks/
http://deeplearning.net/tutorial/lenet.html
https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/
http://neuralnetworksanddeeplearning.com/chap6.html
http://xrds.acm.org/blog/2016/06/convolutional-neural-networks-cnns-illustrated-explanation/
http://andrew.gibiansky.com/blog/machine-learning/convolutional-neural-networks/
https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721#.l6i57z8f2

Join other Wizards in our Slack channel:
http://wizards.herokuapp.com/

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17.12 How to Make a Tensorflow Image Classifier (LIVE)

http://www.youtube.com/watch?v=APmF6qE3Vjc

We're going to build an image classifier using just Tensorflow (no Keras). This will be in depth, the goal for this video is for you to fully understand how a Convolutional Neural Network works. We'll visualize the filters we create along the way as well.

Code for this video: https://github.com/llSourcell/How_to_make_a_tensorflow_image_classifier_LIVE/blob/master/demonotes.ipynb

More CNN learning resources:
http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/
https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/
http://cs231n.github.io/convolutional-networks/
http://deeplearning.net/tutorial/lenet.html
http://neuralnetworksanddeeplearning.com/chap6.html
http://machinelearningmastery.com/crash-course-convolutional-neural-networks/
https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/

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17.13 How to Predict Stock Prices Easily - Intro to Deep Learning #7

http://www.youtube.com/watch?v=ftMq5ps503w

We're going to predict the closing price of the S&P 500 using a special type of recurrent neural network called an LSTM network. I'll explain why we use recurrent nets for time series data, and why LSTMs boost our network's memory power.

Coding challenge for this video:
https://github.com/llSourcell/How-to-Predict-Stock-Prices-Easily-Demo

Vishal's winning code:
https://github.com/erilyth/DeepLearning-SirajologyChallenges/tree/master/Image_Classifier

Jie's runner up code:
https://github.com/jiexunsee/Simple-Inception-Transfer-Learning

More Learning Resources:
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
http://deeplearning.net/tutorial/lstm.html
https://deeplearning4j.org/lstm.html
https://www.tensorflow.org/tutorials/recurrent
http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/
https://blog.terminal.com/demistifying-long-short-term-memory-lstm-recurrent-neural-networks/

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Join other Wizards in our Slack channel:
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https://www.patreon.com/user?u=3191693

music in the intro is chambermaid swing by parov stelar
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17.14 How to Use Tensorflow for Time Series (Live)

http://www.youtube.com/watch?v=hhJIztWR_vo

We're going to use Tensorflow to predict the next event in a time series dataset. This can be applied to any kind of sequential data.

Code for this video:
https://github.com/llSourcell/rnn_tutorial

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More learning resources:
https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series
https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data
https://www.reddit.com/r/MachineLearning/comments/4ervmf/tensorflow_rnn_time_series_prediction/
https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
http://nbviewer.jupyter.org/github/jsseely/tensorflow-rnn-tutorial/blob/master/TensorFlow%20RNN%20tutorial.ipynb

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http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693
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17.15 How to Generate Art - Intro to Deep Learning #8

http://www.youtube.com/watch?v=Oex0eWoU7AQ

We're going to learn how to use deep learning to convert an image into the style of an artist that we choose. We'll go over the history of computer generated art, then dive into the details of how this process works and why deep learning does it so well.

Coding challenge for this video:
https://github.com/llSourcell/How-to-Generate-Art-Demo

Itai's winning code:
https://github.com/etai83/lstm_stock_prediction

Andreas' runner up code:
https://github.com/AndysDeepAbstractions/How-to-Predict-Stock-Prices-Easily-Demo/blob/master/stockdemo.ipynb

More learning resources:
https://harishnarayanan.org/writing/artistic-style-transfer/
https://ml4a.github.io/ml4a/style_transfer/
http://genekogan.com/works/style-transfer/
https://arxiv.org/abs/1508.06576
https://jvns.ca/blog/2017/02/12/neural-style/

Style transfer apps:
http://www.pikazoapp.com/
http://deepart.io/
https://artisto.my.com/
https://prisma-ai.com/

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Join us in the Wizards Slack channel:
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https://www.patreon.com/user?u=3191693

Song at the beginning is called Everyday by Carly Comando
jurassic park inception visualization is from http://www.pyimagesearch.com/2015/07/06/bat-country-an-extendible-lightweight-python-package-for-deep-dreaming-with-caffe-and-convolutional-neural-networks/
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17.16 How to Do Style Transfer with Tensorflow (LIVE)

http://www.youtube.com/watch?v=YoBEGQD3LCc

We're going to learn about all the details of style transfer (especially the math) using just Tensorflow. The goal of this session is for you to understand the details behind how style+content loss is calculated and minimized. We'll also talk about future discoveries.

Code for this video:
https://github.com/llSourcell/How_to_do_style_transfer_in_tensorflow

Learning resources:
http://www.makeuseof.com/tag/create-neural-paintings-deepstyle-ubuntu/
https://blog.paperspace.com/art-with-neural-networks/
https://www.tensorflow.org/versions/r0.11/how_tos/
https://no2147483647.wordpress.com/2015/12/21/deep-learning-for-hackers-with-mxnet-2/
https://code.facebook.com/posts/196146247499076/delivering-real-time-ai-in-the-palm-of-your-hand/
http://kawahara.ca/deep-dreams-and-a-neural-algorithm-of-artistic-style-slides-and-explanations/
http://www.chioka.in/tensorflow-implementation-neural-algorithm-of-artistic-style

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17.17 How to Generate Music - Intro to Deep Learning #9

http://www.youtube.com/watch?v=4DMm5Lhey1U

We're going to build a music generating neural network trained on jazz songs in Keras. I'll go over the history of algorithmic generation, then we'll walk step by step through the process of how LSTM networks help us generate music.

Coding Challenge for this video:
https://github.com/llSourcell/How-to-Generate-Music-Demo

Vishal's Winning Code:
https://github.com/erilyth/DeepLearning-SirajologyChallenges/tree/master/Art_Generation

Michael's Runner up code:
https://github.com/michalpelka/How-to-Generate-Art-Demo/blob/master/demo.ipynb

More Learning Resources:
https://medium.com/@shiyan/understanding-lstm-and-its-diagrams-37e2f46f1714#.swstv6z61
http://mourafiq.com/2016/05/15/predicting-sequences-using-rnn-in-tensorflow.html
https://magenta.tensorflow.org/2016/06/10/recurrent-neural-network-generation-tutorial/
http://deeplearning.net/tutorial/rnnrbm.html
https://maraoz.com/2016/02/02/abc-rnn/
http://www.cs.cmu.edu/~music//cmsip/slides/05-algo-comp.pdf
http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/
https://www.reddit.com/r/algorithmicmusic/

Please Subscribe! And like. And comment. That's what keeps me going.

Join us in the Wizards Slack channel:
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And please support me on Patreon:
https://www.patreon.com/user?u=3191693

Thanks Ji-Sung Kim for the example code:
https://deepjazz.io
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17.18 How to Generate Music with Tensorflow (LIVE)

http://www.youtube.com/watch?v=pg9apmwf7og

This live session will focus on the details of music generation using the Tensorflow library. The goal is for you to understand the details of how to encode music, feed it to a well tuned model, and use it to generate really cool sounds. And I'm going to NOT use Google Hangouts, instead I'll do this with a green screen and a DSLR camera :)

Code for this video:
https://github.com/llSourcell/music_demo_live/

Please subscribe! And like. And comment. That's what keeps me going.

My Udacity course is open for enrollments until this Saturday at midnight:
https://www.udacity.com/course/deep-learning-nanodegree-foundation--nd101

More Learning Resources:
http://www.asimovinstitute.org/analyzing-deep-learning-tools-music/
http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/
https://github.com/hexahedria/biaxial-rnn-music-composition
http://www.hexahedria.com/2016/08/08/summer-research-on-the-hmc-intelligent-music-software-team
https://magenta.tensorflow.org/
https://github.com/farizrahman4u/seq2seq
http://stackoverflow.com/questions/14448380/how-do-i-read-a-midi-file-change-its-instrument-and-write-it-back
https://github.com/vishnubob/python-midi

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Please support me on Patreon:
https://www.patreon.com/user?u=3191693

Streaming Live from UploadVR's Studio in San Francisco!: https://www.youtube.com/uploadvr
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17.19 How to Make a Text Summarizer - Intro to Deep Learning #10

http://www.youtube.com/watch?v=ogrJaOIuBx4

I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory.

Code for this video (Challenge included):
https://github.com/llSourcell/How_to_make_a_text_summarizer

Jie's Winning Code:
https://github.com/jiexunsee/rudimentary-ai-composer

More Learning resources:
https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully
https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html
https://en.wikipedia.org/wiki/Automatic_summarization
http://deeplearning.net/tutorial/rnnslu.html
http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/

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17.20 How to Generate Your Own Wikipedia Articles (LIVE)

http://www.youtube.com/watch?v=ZGU5kIG7b2I

We're going to build an LSTM network in Tensorflow (no Keras) to generate text after training on Wikipedia articles. You'll learn how an LSTM cell works programmatically since we'll build one using TF's math functions and how you can parse a similar dataset

Code:
https://github.com/llSourcell/wiki_generator_live

Dataset: https://metamind.io/research/the-wikitext-long-term-dependency-language-modeling-dataset/


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Learning resources:
http://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
https://github.com/jsseely/tensorflow-rnn-tutorial/blob/master/TensorFlow%20RNN%20tutorial.ipynb
https://chunml.github.io/ChunML.github.io/project/Creating-Text-Generator-Using-Recurrent-Neural-Network/
http://deeplearningathome.com/2016/10/Text-generation-using-deep-recurrent-neural-networks.html
https://larseidnes.com/2015/10/13/auto-generating-clickbait-with-recurrent-neural-networks/
http://genekogan.com/works/learning-sequences/

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17.21 How to Make a Language Translator - Intro to Deep Learning #11

http://www.youtube.com/watch?v=nRBnh4qbPHI

Let's build our own language translator using Tensorflow! We'll go over several translation methods and talk about how Google Translate is able to achieve state of the art performance.

Code for this video:
https://github.com/llSourcell/How_to_make_a_language_translator

Ryan's Winning Code:
https://github.com/rtlee9/recipe-summarization

Sarah's Runner-up Code:
https://github.com/scollins83/teal_deer

More Learning Resources:
https://medium.com/@ageitgey/machine-learning-is-fun-part-5-language-translation-with-deep-learning-and-the-magic-of-sequences-2ace0acca0aa
https://www.tensorflow.org/tutorials/seq2seq
https://devblogs.nvidia.com/parallelforall/introduction-neural-machine-translation-with-gpus/
https://www.youtube.com/watch?v=vxibD6VaOfI
http://neural-monkey.readthedocs.io/en/latest/machine_translation.html
http://blog.systransoft.com/how-does-neural-machine-translation-work/
http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/
https://blog.altoros.com/enabling-multilingual-neural-machine-translation-with-tensorflow.html
https://www.quora.com/How-can-I-build-a-machine-translation-system
https://blog.heuritech.com/2016/01/20/attention-mechanism/
https://smerity.com/articles/2016/google_nmt_arch.html

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Credits to Biggi Hilmars for the intro tune
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17.22 How to Use Tensorflow for Seq2seq Models (LIVE)

http://www.youtube.com/watch?v=ElmBrKyMXxs

Let's build a Sequence to Sequence model in Tensorflow to learn exactly how they work. You can use this model to make chatbots, language translators, text generators, and much more . We'll go over memory, attention, and some variants (like bidirectional layers) both programmatically and mathematically.

Code for this video:
https://github.com/llSourcell/seq2seq_model_live/blob/master/2-seq2seq-advanced.ipynb

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More Learning resources:
https://www.tensorflow.org/tutorials/seq2seq
http://www.kdnuggets.com/2015/06/rnn-tutorial-sequence-learning-recurrent-neural-networks.html
http://suriyadeepan.github.io/2016-06-28-easy-seq2seq/
https://indico.io/blog/sequence-modeling-neuralnets-part1/
http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/

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http://wizards.herokuapp.com/

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17.23 How to Make a Chatbot - Intro to Deep Learning #12

http://www.youtube.com/watch?v=t5qgjJIBy9g

Lets Make a Question Answering chatbot using the bleeding edge in deep learning (Dynamic Memory Network). We'll go over different chatbot methodologies, then dive into how memory networks work, with accompanying code in Keras.

Code + Challenge for this video:
https://github.com/llSourcell/How_to_make_a_chatbot

Nemanja's Winning Code:
https://github.com/Nemzy/language-translation/blob/master/neural_machine_translation.ipynb

Vishal's Runner up code:
https://github.com/erilyth/DeepLearning-Challenges/tree/master/Language_Translation

Web app to run the code yourself:
https://ethancaballero.pythonanywhere.com

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More Learning resources:
https://www.youtube.com/watch?v=FCtpHt6JEI8&t=643s
https://www.youtube.com/watch?v=Qf0BqEk5n3o&t=637s
https://yerevann.github.io/2016/02/05/implementing-dynamic-memory-networks/
https://www.youtube.com/watch?v=2A5DKPA5lAw
http://www.wildml.com/2016/01/attention-and-memory-in-deep-learning-and-nlp/
https://github.com/domluna/memn2n

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17.24 How to Use Tensorboard (LIVE)

http://www.youtube.com/watch?v=fBVEXKp4DIc

We're going to learn how the visualizer that comes with Tensorflow works in this live stream. We'll go through a bunch of different features and test out its functionality both programmatically and visually.

4:41 code begins
37:07 tensorboard visualization begins

Code for this video:
https://github.com/llSourcell/how_to_use_tensorboard_live/tree/master

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More Learning resources:
https://www.tensorflow.org/get_started/summaries_and_tensorboard
http://ischlag.github.io/2016/06/04/how-to-use-tensorboard/
https://www.youtube.com/watch?v=3bownM3L5zM
https://blog.altoros.com/visualizing-tensorflow-graphs-with-tensorboard.html
http://www.titiapps.com/hands-on-tensorboard-tensorflow-dev-summit-2017/

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17.25 How to Win Slot Machines - Intro to Deep Learning #13

http://www.youtube.com/watch?v=AIeWLTUYLZQ

We'll learn how to solve the multi-armed bandit problem (maximizing success for a given slot machine) using a reinforcement learning technique called policy gradients.

Code for this video:
https://github.com/llSourcell/how_to_win_slot_machines

Mike's winning code:
https://github.com/xkortex/Siraj_Chatbot_Challenge

Vishal's runner up code:
https://github.com/erilyth/DeepLearning-Challenges/tree/master/Text_Based_Chatbot

this coding challenge was really close, so i'm also going to put code for 3rd place just this time (Eibriel):
https://github.com/Eibriel/ice-cream-truck

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More Learning resources:
http://karpathy.github.io/2016/05/31/rl/
http://minpy.readthedocs.io/en/latest/tutorial/rl_policy_gradient_tutorial/rl_policy_gradient.html
http://pemami4911.github.io/blog/2016/08/21/ddpg-rl.html
http://kvfrans.com/simple-algoritms-for-solving-cartpole/
https://medium.com/@awjuliani/super-simple-reinforcement-learning-tutorial-part-1-fd544fab149
https://dataorigami.net/blogs/napkin-folding/79031811-multi-armed-bandits

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17.26 How to Beat Pong Using Policy Gradients (LIVE)

http://www.youtube.com/watch?v=PDbXPBwOavc

We're going to use the policy gradient technique from reinforcement learning to beat the game of Pong. We'll use OpenAI's Universe as an environment for our agent and I'll go over the process of setting it up as well as the math behind the PG method in detail.

Microphone popping issues end at 11:15 . That cannot happen again. Udacity is aware of this and will be more prepared next time.

Code for this video:
https://github.com/llSourcell/Policy_Gradients_to_beat_Pong

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

More Learning resources:
http://www.scholarpedia.org/article/Policy_gradient_methods
http://proceedings.mlr.press/v32/silver14.pdf
http://karpathy.github.io/2016/05/31/rl/
http://home.deib.polimi.it/restelli/MyWebSite/pdf/rl7.pdf
http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching_files/pg.pdf
https://github.com/dennybritz/reinforcement-learning/tree/master/PolicyGradient

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17.27 How to Generate Images - Intro to Deep Learning #14

http://www.youtube.com/watch?v=3-UDwk1U77s

We're going to build a variational autoencoder capable of generating novel images after being trained on a collection of images. We'll be using handwritten digit images as training data. Then we'll both generate new digits and plot out the learned embeddings. And I introduce Bayesian theory for the first time in this series :)

Code for this video:
https://github.com/llSourcell/how_to_generate_images

Mike's Winning Code:
https://github.com/xkortex/how_to_win_slot_machines/blob/master/WallStBandits.ipynb

SG's Runner up Code:
https://github.com/esha-sg/Intro-DeepLearning-Siraj-Week13

Please subscribe! And like. And comment. That's what keeps me going.

2 things
-The embedding visualization at the end would be more spread out if i trained it for more epochs (50 is recommended) but i just used 5.
-The code in the video doesn't fully implement the reparameterization trick (to save space) but check the GitHub repo for details on that.

More Learning resources:
https://jaan.io/what-is-variational-autoencoder-vae-tutorial/
http://kvfrans.com/variational-autoencoders-explained/
http://blog.fastforwardlabs.com/2016/08/12/introducing-variational-autoencoders-in-prose-and.html
http://blog.fastforwardlabs.com/2016/08/22/under-the-hood-of-the-variational-autoencoder-in.html
http://blog.evjang.com/2016/11/tutorial-categorical-variational.html
https://jmetzen.github.io/2015-11-27/vae.html

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17.28 How to Generate Images with Tensorflow (LIVE)

http://www.youtube.com/watch?v=iz-TZOEKXzA

We'll build a Variational Autoencoder using Tensorflow to generate images. We'll go through several examples including digit images and pokemon images. VAE's allow us to generate, compress, denoise, and even fuse images together. They are an incredibly powerful tool and we'll go over the implementation details (math included) in this live session.

Code: https://github.com/llSourcell/how_to_generate_images_with_tensorflow_LIVE

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More Learning resources:
https://arxiv.org/abs/1606.05908
https://github.com/stitchfix/fauxtograph
http://deeplearning.jp/cvae/
https://ift6266h17.wordpress.com/2017/03/26/q3-reparameterization-trick-of-variational-autoencoder/
https://www.quora.com/What-is-the-latent-loss-in-variational-autoencoders
https://www.slideshare.net/ShaiHarel/variational-autoencoder-talk

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17.29 How to Generate Video - Intro to Deep Learning #15

http://www.youtube.com/watch?v=-E2N1kQc8MM

Generative Adversarial Networks. It's time. We're going to use a Deep Convolutional GAN to generate images of the alien language from the movie arrival that we can then stitch together to animate into video. I'll go over the architecture of a GAN and then we'll implement one ourselves!

Code for this video (coding challenge included):
https://github.com/llSourcell/how_to_generate_video

Nemanja's winning code:
https://github.com/Nemzy/video_generator

Niyas' Runner up code:
https://github.com/niazangels/vae-pokedex

and his blog post:
https://hackernoon.com/how-to-autoencode-your-pok%C3%A9mon-6b0f5c7b7d97

Please Subscribe! And like. and comment. That's what keeps me going.

More Learning Resources:
http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/
https://blog.openai.com/generative-models/
http://cs.stanford.edu/people/karpathy/gan/
https://channel9.msdn.com/Events/Neural-Information-Processing-Systems-Conference/Neural-Information-Processing-Systems-Conference-NIPS-2016/Generative-Adversarial-Networks
http://wiseodd.github.io/techblog/2016/09/17/gan-tensorflow/
https://www.slideshare.net/ThomasDaSilvaPaula/a-very-gentle-introduction-to-generative-adversarial-networks-aka-gans-71614428
http://blog.evjang.com/2016/06/generative-adversarial-nets-in.html
https://medium.com/@awjuliani/generative-adversarial-networks-explained-with-a-classic-spongebob-squarepants-episode-54deab2fce39

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17.30 Generative Adversarial Networks (LIVE)

http://www.youtube.com/watch?v=0VPQHbMvGzg

We're going to build a GAN to generate some images using Tensorflow. This will help you grasp the architecture and intuition behind adversarial approaches to machine learning. We're building a Deep Convolutional GAN to generate MNIST digits.

Code for this video:
https://github.com/llSourcell/Generative_Adversarial_networks_LIVE/blob/master/EZGAN.ipynb

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More Learning resources:
http://guimperarnau.com/blog/2017/03/Fantastic-GANs-and-where-to-find-them
http://www.cs.toronto.edu/~dtarlow/pos14/talks/goodfellow.pdf
https://datawarrior.wordpress.com/2017/02/03/generative-adversarial-networks/
https://www.quora.com/What-are-Generative-Adversarial-Networks
http://nuit-blanche.blogspot.com/2017/01/nips-2016-tutorial-generative.html
http://www.paddlepaddle.org/develop/doc/tutorials/gan/index_en.html
http://gkalliatakis.com/blog/delving-deep-into-gans

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17.31 How to Convert Text to Images - Intro to Deep Learning #16

http://www.youtube.com/watch?v=gmvRStL_Dag

Generative Adversarial Networks are back! We'll use the cutting edge StackGAN architecture to let us generate images from text descriptions alone. This is pretty wild stuff and there is so much room for improvement. The possibilities are endless. I'll go through the architecture, code, and the implications of this technology for humanity.

Special shoutout to new Patrons Joshua Tobkin, Cameron Tofer, and Zarathustra Technologies. I'll add you guys to the credits next video.

Code for this video:
https://github.com/llSourcell/how_to_convert_text_to_images

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More learning resources:
https://www.youtube.com/watch?v=rAbhypxs1qQ
https://www.youtube.com/watch?v=93yaf_kE0Fg
https://arxiv.org/abs/1612.03242
http://cs.stanford.edu/people/karpathy/gan/
http://blog.evjang.com/2016/06/generative-adversarial-nets-in.html

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693
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17.32 Generative Adversarial Networks for Style Transfer (LIVE)

http://www.youtube.com/watch?v=MgdAe-T8obE

Generative Adversarial Nets are such a rich topic for exploration, we're going to build one that was released just 2 months ago called the "DiscoGAN" that lets us transfer the style between 2 datasets. And I'll be building this using Tensorflow.

Code for this video:
https://github.com/llSourcell/GANS-for-style-transfer

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More learning resources:
https://arxiv.org/abs/1703.05192
https://github.com/SKTBrain/DiscoGAN
https://www.reddit.com/r/MachineLearning/comments/5zp0eu/r_170305192_learning_to_discover_crossdomain/
https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7

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17.33 How to Learn from Little Data - Intro to Deep Learning #17

http://www.youtube.com/watch?v=tChcZpBbTTA

One-shot learning! In this last weekly video of the course, i'll explain how memory augmented neural networks can help achieve one-shot classification for a small labeled image dataset. We'll also go over the architecture of it's inspiration (the neural turing machine).

Code for this video (with challenge):
https://github.com/llSourcell/How-to-Learn-from-Little-Data

Please subscribe! And like. And comment. That's what keeps me going.

More learning resources:
https://www.youtube.com/watch?v=CzQSQ_0Z-QU
https://arxiv.org/abs/1605.06065
https://futuristech.info/posts/differential-neural-computer-from-deepmind-and-more-advances-in-backward-propagation
https://thenewstack.io/googles-deepmind-ai-now-capable-deep-neural-reasoning/

Join us in the Wizards Slack Channel:
http://wizards.herokuapp.com/

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17.34 Differentiable Neural Computer (LIVE)

http://www.youtube.com/watch?v=r5XKzjTFCZQ

The Differentiable Neural Computer is an awesome model that DeepMind recently released. It's a memory augmented network that can perform meta-learning (learning to learn). We'll go over it's architecture details and implement it ourselves in Tensorflow.

Code for this video: https://github.com/llSourcell/differentiable_neural_computer

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More learning resources:
https://deepmind.com/blog/differentiable-neural-computers/
https://www.quora.com/How-groundbreaking-is-DeepMinds-Differentiable-neural-network
https://github.com/dsindex/blog/wiki/%5Bdnc%5D-Differentiable-Neural-Computer
https://blog.acolyer.org/2016/03/09/neural-turing-machines/
https://thenewstack.io/googles-deepmind-ai-now-capable-deep-neural-reasoning/

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18 Game Bots

18.1 Build a Game AI - Machine Learning for Hackers #3

http://www.youtube.com/watch?v=HBAUeJkFMH0

This video will get you up and running with your first game AI in just 10 lines of Python. The AI can theoretically learn to master any game you train it on, but has only been tested on 2D Atari games so far.

The code for this video is here:
https://github.com/llSourcell/Game-AI

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Tensorflow install instructions here:
https://www.tensorflow.org/versions/r0.8/get_started/os_setup.html#pip-installation

Gym install instructions here:
https://gym.openai.com/docs

Great course on the brain (I really love this course):
https://www.mcb80x.org/

Original Deep Q Learner Paper:
https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf

Lots of info on convolutional neural networks:
http://cs231n.github.io/convolutional-networks/

Lots of info on reinforcement learning:
http://www.nervanasys.com/demystifying-deep-reinforcement-learning/

I'm a fan of www.fomoro.com for cloud GPU computing since they are the only free-to-try cloud GPU provider I could find. Let me know if you find another!

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment.
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18.2 Build a Game Bot (LIVE)

http://www.youtube.com/watch?v=3vxk91K1PiI

This is my first live stream ever. I'm going to be using OpenAI's Gym library to build a bot that gets better and better at playing a 2D game like Pac-Man.I'll also just be live to answer any questions about me, my life, and whatever else you guys wanna talk about!

Code for this video:
https://github.com/llSourcell/build_a_game_bot_live

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

OpenAI Gym:
https://github.com/openai/gym

An article with in-depth explanations:
http://kvfrans.com/simple-algoritms-for-solving-cartpole/

Patreon:
https://www.patreon.com/user?u=3191693
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18.3 Pong Neural Network (LIVE)

http://www.youtube.com/watch?v=Hqf__FlRlzg

In this video we're going to build the popular game Pong from scratch in Python, then train a neural network to become an unbeatable 2nd player! We use Tensorflow to build our neural net and pygame to build our Pong game.

The full, working code for this video is here:
https://github.com/llSourcell/pong_neural_network_live

Unlike my previous 2 live sessions where i did less than 60 lines of code each, I tried to do about 400 lines of code in this one. So I didn't have time to get to everything!

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Please share this video, like, comment and subscribe! And please support me on Patreon!:
https://www.patreon.com/user?u=3191693

That's what keeps me going. I love you all.
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18.4 How to Make an Amazing Video Game Bot Easily

http://www.youtube.com/watch?v=mGYU5t8MO7s

In this video, we first go over the history of video game AI, then I introduce OpenAI's Universe, which lets you build a bot that can play thousands of different video games. It has environments for all sorts of games, from Space Invaders, to Grand Theft Auto, to Protein folding simulations.

CODING CHALLENGE DUE DATE: Thursday, December 15th.
(which is 2 weeks, not 1 week from now like usual)

The coding challenge for this video is to make a bot that's better than this video's demo code. Post your Github link in the comments! For your README, just include a 1-3 sentence description of your strategy and instructions on how to run the code.The demo code can be found in the README of the Universe repo. :
https://github.com/openai/universe

And a Tensorflow based starter bot can be found here:
https://github.com/openai/universe-starter-agent

Some great learning resources:
https://www.nervanasys.com/openai/
http://karpathy.github.io/2016/05/31/rl/
http://kvfrans.com/simple-algoritms-for-solving-cartpole/
https://kofzor.github.io/Reinforcement_Learning_101/

Join other Wizards on our Slack channel:
https://wizards.herokuapp.com/

OpenAI asked me to make this video and I gladly said yes. They are awesome!!

Please subscribe! And like and comment. That's what keeps me going.

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18.5 How to Make an Asteroids Game Bot (LIVE)

http://www.youtube.com/watch?v=h2qVYpK6TPE

In this video, we'll make a bot using a technique called 'Neuroevolution' to defeat the popular game of asteroids! This will be in Javascript. I talk about the architecture (neurons, layers, networks, genomes, generations) and the interesting actions we'll take to improve our bot (like breeding and mutation).

The code for this video is here:
https://github.com/llSourcell/asteroids_game_bot_LIVE

More learning resources:
http://eplex.cs.ucf.edu/hyperNEATpage/
http://stackoverflow.com/questions/31708478/how-to-evolve-weights-of-a-neural-network-in-neuroevolution
http://nn.cs.utexas.edu/?neuroevolution

Shoutout to Daniel Shiffman! This is apart of a collaboration with him:
https://www.youtube.com/watch?v=hacZU523FyM&t=94s

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18.6 How to Install OpenAI's Universe and Make a Game Bot [LIVE]

http://www.youtube.com/watch?v=XI-I9i_GzIw

I'm going to go through the steps necessary to install OpenAI's Universe, then we'll build our own game bot using reinforcement learning. This code will be in Python.

*Update - I said something big was coming out this Friday in this video. I just got out of a meeting with the PR team i'm working with for promotion and they pleaded with me to release it next Friday (1/13) instead. So that''ll happen then. Just wanted to let you guys know.

Code for this video:
https://github.com/llSourcell/OpenAI_Game_Bot_Live_stream

Please Subscribe! And Like. And Comment. That's what keeps me going.

Some past submissions for the 'Make a Game Bot' Challenge using OpenAI's Universe':
https://github.com/av80r/coaster_racer_coding_challenge (winner)
https://github.com/rhnvrm/universe-coaster-racer-challenge

More Learning Resources:
https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0
https://www.youtube.com/watch?v=mGYU5t8MO7s
http://karpathy.github.io/2016/05/31/rl/
http://www.wildml.com/category/reinforcement-learning/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
Follow me:
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18.7 How to use Q Learning in Video Games Easily

http://www.youtube.com/watch?v=A5eihauRQvo

In this video, I go over the history of reinforcement learning then talk about how a type of reinforcement learning called Q learning works. We'll then write a 10 line python script for a Q learning bot in a 5x5 grid that will help it go from point A to point B as fast as possible.

The coding challenge for this video is here:
https://github.com/llSourcell/q_learning_demo

More Learning resources:
http://hunch.net/~jl/projects/RL/RLTheoryTutorial.pdf
http://www2.econ.iastate.edu/tesfatsi/RLUsersGuide.ICAC2005.pdf
https://www.quora.com/What-are-some-good-tutorials-on-reinforcement-learning
http://burlap.cs.brown.edu/tutorials/cpl/p3.html
http://outlace.com/Reinforcement-Learning-Part-1/
http://firsttimeprogrammer.blogspot.com/2016/09/getting-ai-smarter-with-q-learning.html

Join us in our Slack channel:
http://wizards.herokuapp.com/

In the last live stream I said there would be a big reveal, that reveal is coming next Friday (1/13).

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19 The Beauty of Math

19.1 Why is P vs NP Important?

http://www.youtube.com/watch?v=9MvbNPQiEE8

In this video, I explain perhaps the most famous problem in all of Computer Science. Does P = NP? I define the terms and give examples of each. We also programmatically go through the traveling salesman problem. I experiment with a little bit of mixed reality in this video as well.

Code for this video:
https://github.com/llSourcell/p_vs_np_challenge

Nichole's winning code:
https://github.com/nhrigby

Mick's runner-up code:
https://github.com/mickvanhulst

Join the Wizard's Slack Channel:
https://wizards.herokuapp.com/

Some more great P vs NP resources:
https://danielmiessler.com/study/pvsnp/
https://qntm.org/pnp
http://news.mit.edu/2009/explainer-pnp
https://blog.codinghorror.com/the-girl-who-proved-p-np/
https://medium.com/the-physics-arxiv-blog/the-astounding-link-between-the-p-np-problem-and-the-quantum-nature-of-universe-7ef5eea6fd7a

Please subscribe! And like and comment and share. That's what keeps me going.

And please support me on Patreon!
https://www.patreon.com/user?u=3191693

I used the Tilt Brush mixed reality app to draw the complexity classes for fun. Thanks Az Balabanian and the Upload Collective for letting me shoot videos in VR! :
https://www.Azadux.com/mixed-reality
https://www.Uploadcollective.com
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19.2 Beautiful Mathematics in the Natural World

http://www.youtube.com/watch?v=b3kA3Yn5VWY

This is my end of the year video! I wanted to do something a little different. I traveled to Portland, Oregon for a week to explore/meet people and decided to use the shots I took while hiking to illustrate how math is all around us in the natural world. We can and will discover the rules of intelligence. The fact that it is governed by mathematics only makes it that much more beautiful. From simple rules emerge incredible complexity.

Vishal's Winning code:
https://github.com/erilyth/visualize_dataset_demo

Sethu's runner up code:
https://github.com/sethuiyer/visualize-GOT

Original peer-reviewed paper in Science mag by Cambridge Professor Stolum (cited 221 times) on how the average sinuosity of all rivers is pi:
http://raaf.org/pdfs/meandering_river.pdf

More Learning Resources:
https://www.comsol.com/multiphysics/navier-stokes-equations
https://www.theguardian.com/science/2016/nov/21/magic-numbers-can-maths-equations-be-beautiful
https://westhunt.wordpress.com/2013/06/07/the-breeders-equation/
https://www.youtube.com/watch?v=GzCvlFRISIM

Join us in the Wizards Slack Channel:
http://wizards.herokuapp.com/

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And please support me on Patreon:
https://www.patreon.com/user?u=3191693

The background music is the Interstellar theme by Hans Zimmer
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19.3 How to Prevent an AI Apocalypse

http://www.youtube.com/watch?v=fLWnCjOvcwg

I traveled to Amsterdam for a week to speak at The Next Web Conference on AI Safety. While roaming the streets of the city, I decided to take some shots and formulate a video on the same topic for you guys. In the battle of good vs evil, it's up to our community to ensure good wins. I'll resume the coding videos next week when I get back to San Francisco.

Please Subscribe! And like. And comment. That's what keeps me going.

I'll post a link to the talk once it's up, here's an article in the mean time:
https://thenextweb.com/artificial-intelligence/2017/05/18/how-to-keep-ai-from-killing-us-all/#.tnw_VaEi7vjZ

More Learning resources:
https://futureoflife.org/ai-safety-research/
https://iamtrask.github.io/2017/03/17/safe-ai/
https://blog.openai.com/concrete-ai-safety-problems/
https://intelligence.org/why-ai-safety/
https://80000hours.org/career-reviews/artificial-intelligence-risk-research/
https://foundational-research.org/files/suffering-focused-ai-safety.pdf

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon: https://www.patreon.com/user?u=3191693
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20 Intro to Data Analysis

20.1 The Best Way to Prepare a Dataset Easily

http://www.youtube.com/watch?v=0xVqLJe9_CY

In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it). The example I use is preparing a dataset of brain scans to classify whether or not someone is meditating.

The challenge for this video is here:
https://github.com/llSourcell/prepare_dataset_challenge

Carl's winning code:
https://github.com/av80r/coaster_racer_coding_challenge

Rohan's runner-up code:
https://github.com/rhnvrm/universe-coaster-racer-challenge

Come join other Wizards in our Slack channel:
http://wizards.herokuapp.com/

Dataset sources I talked about:
https://github.com/caesar0301/awesome-public-datasets
https://www.kaggle.com/datasets
http://reddit.com/r/datasets

More learning resources:
https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-prepare-data
http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/
https://www.youtube.com/watch?v=kSslGdST2Ms
http://freecontent.manning.com/real-world-machine-learning-pre-processing-data-for-modeling/
http://docs.aws.amazon.com/machine-learning/latest/dg/step-1-download-edit-and-upload-data.html
http://paginas.fe.up.pt/~ec/files_1112/week_03_Data_Preparation.pdf

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And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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20.2 The Best Way to Visualize a Dataset Easily

http://www.youtube.com/watch?v=yQsOFWqpjkE

In this video, we'll visualize a dataset of body metrics collected by giving people a fitness tracking device. We'll go over the steps necessary to preprocess the data, then use a technique called T-SNE to reduce the dimensionality of our data so we can visualize it.

Code + challenge for this video:
https://github.com/llSourcell/visualize_dataset_demo

Keagan's winning code:
https://github.com/WeldFire/prepare_dataset_challenge

Vishal's runner-up code:
https://github.com/erilyth/Pokemon-Type-Classification-Challenge

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Live T-SNE demo in the browser:
http://cs.stanford.edu/people/karpathy/tsnejs/

More learning resources:
https://www.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm
https://indico.io/blog/visualizing-with-t-sne/
http://blog.applied.ai/visualising-high-dimensional-data/
http://machinelearningmastery.com/visualize-machine-learning-data-python-pandas/

Please subscribe! And like. And comment. That's what keeps me going.

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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20.3 Visualizing Data with D3.js (LIVE)

http://www.youtube.com/watch?v=sEpRzyPRH0s

In this session, I'm going to show you how to visualize data using the popular data visualization library (D3.js). This is useful for showing your results from machine learning algorithms, or just for you to understand what your data looks like. We visualize survivor stats from the popular Kaggle Titanic Survivor dataset.

The code in this video can be found here:
https://github.com/llSourcell/D3_Data_visualization_live

Please support me on Patreon! I want to continue to do this Youtube channel full-time:
https://www.patreon.com/user?u=3191693

Comment, Like, and Subscribe! That's what keeps me going. :)
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20.4 5 Steps to Pass Data Science Interviews

http://www.youtube.com/watch?v=OHhoLhYW2cg

Data Science is becoming more and more popular as a career choice since it offers both lucrative salaries and the opportunity to have high impact. The Data Science interview process is challenging, but with dedicated practice you can succeed. In this video, I'll outline the 7 steps to pass any Data Science Interview. We'll go over topics like studying techniques, portfolio optimization, and interviewing tips, all of which are prominent in the modern Data Science interview pipeline. I've listed all of the resources I've mentioned both in the video description and in the associated GitHub readme. Enjoy!

Plan for this video:
https://github.com/llSourcell/Data_Science_Interview_Guide

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Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
instagram: https://www.instagram.com/sirajraval
Facebook: https://www.facebook.com/sirajology

Step 1 - Ask yourself "Why"

Investor Insights for Inspiration
https://thesyndicate.vc/the-top-5-startup-technology-angel-investing-and-venture-capital-podcasts-in-order/

Investors to follow on Twitter
http://www.venturearchetypes.com/faq/Investors-On-Twitter-List.html

Data Stories Podcasts
http://datastori.es/

Step 2 - Create and Execute a Study Plan

Learn Data Science in 3 Months
https://www.youtube.com/watch?v=9rDhY1P3YLA

Chromebook Data Science
https://leanpub.com/universities/set/jhu/chromebook-data-science

Open Source University
https://github.com/ossu/data-science

Practice Technical Interviews
http://interviewing.io

Use Flashcards
https://www.brainscape.com/subjects/data-science

Find a Mentor
https://www.sharpestminds.com

Step 3 - Build a Portfolio

Design a resume + personal website
https://www.youtube.com/watch?v=nMK94JlKRb4

And have 3 projects on Github, one should have a web presence

Step 4 - Start Pitching for Jobs

- https://www.angel.co
- Ask friends on Social Media
- https://www.ventureloop.com/ventureloop/job_search.php (VC Portfolio companies job listings)
- https://news.ycombinator.com/
- Find recruiters using
"site:linkedin.com quora technical recruiter"
but replace quora with your company

Scheduling tool
https://calendly.com/sirajraval/

Step 5 - Complete the Interview

Study Data Science Interview Questions on Glassdoor
https://www.glassdoor.com/Interview/data-scientist-interview-questions-SRCH_KO0,14.htm

----------------

Join us at the School of AI:
https://theschool.ai/

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#DataScienceInterview #SirajRaval #MachineLearning

21 Really Quick Questions

21.1 Joel Shor - Really Quick Questions with an Awesome Google Engineer

http://www.youtube.com/watch?v=Fwu7GozZukU

I ask 67 questions to Google Engineer and AI researcher Joel Shor as we take a stroll through the Googleplex in Mountain View, California. Joel and I have been friends for a while and he gladly agreed to an interview. I ask him questions that range from his sleeping patterns to his deep learning library of choice (tensorflow)

Please hit that subscribe button if you liked this interview!

And like. And comment. That's what keeps me going.

Join other Wizards on our Slack channel:
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Joel's team recently released a paper on image compression using recurrent neural nets:
http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/45534.pdf
https://research.googleblog.com/2016/09/image-compression-with-neural-networks.html

Here's his Linkedin:
https://www.linkedin.com/in/joel-shor-20552260

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21.2 Catherine Olsson - Really Quick Questions with an OpenAI Engineer

http://www.youtube.com/watch?v=24z0c6HLw9A

I ask 67 questions to OpenAI Engineer Catherine Olsson as we take a stroll around OpenAI HQ in San Francisco. Catherine graciously agreed to an interview right after the release of OpenAI's Universe. I ask her questions that range from her deepest fears to her favorite Operating System.

Please hit that subscribe button if you liked this interview!

And like. And comment. That's what keeps me going.

Join other Wizards on our Slack channel:
http://wizards.herokuapp.com/

Check out OpenAI's new Universe release:
https://github.com/openai/universe

Here's her Linkedin:
https://www.linkedin.com/in/catherineolsson

Side note - I just changed my youtube username to my full name, since that's what I want to be known by.

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21.3 Really Quick Questions with Sebastian Thrun

http://www.youtube.com/watch?v=mcKeMTNl9hQ

I ask 67 questions to the founder of Google X, self-driving car pioneer, former Director of the Stanford AI lab, and President of Udacity Sebastian Thrun. We take a stroll around Udacity HQ in Mountain View, California.

Please hit that subscribe button if you liked this interview!

And like. And comment. That's what keeps me going.

Join other Wizards on our Slack channel:
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More on Sebastian:
https://en.wikipedia.org/wiki/Sebastian_Thrun

lol he answered 'the matrix' as his favorite movie too. the pattern is real guys.

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21.4 Dandelion Mane - Really Quick Questions with a Tensorflow Engineer

http://www.youtube.com/watch?v=axRHotkkTVI

I ask 67 questions to Dandelion Mane as we walk around Google HQ in Mountain View, California. Dandelion used to be my roommate and is now working on the Tensorflow team at Google. Specifically, he works on the visualizer called Tensorboard. I ask him a lot of rapid fire questions, some machine learning related and some about his life in general.

Please hit that subscribe button if you liked this interview!

And like. And comment. That's what keeps me going.

Join other Wizards on our Slack channel:
http://wizards.herokuapp.com/

More info on Dandelion:
https://www.linkedin.com/in/danmane

His recent talk on Tensorboard apart of the Tensorflow Dev Summit
https://www.youtube.com/watch?v=eBbEDRsCmv4&t=165s

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21.5 Really Quick Questions with Siraj Raval

http://www.youtube.com/watch?v=XtHwjrm4sMg

I crowdsourced questions on Twitter, and picked 67 of them for an interview with myself! I had my friend Daniel Rigberg ask the questions and film me.

Please Subscribe! And like. And Comment. That's what keeps me going.

Resources I've mentioned:

Deep Learning Book:
http://www.deeplearningbook.org/

My playlists/courses:
https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A/playlists

Cormac McCarthy's Book:
https://www.amazon.com/Road-Cormac-McCarthy/dp/0307387895

Udacity Nanodegree:
https://www.udacity.com/course/deep-learning-nanodegree-foundation--nd101

Andrew Ng's Course:
https://www.coursera.org/specializations/deep-learning?siteID=SAyYsTvLiGQ-ZwaOIEdDzM4pwFY43MoUFQ&utm_content=10&utm_medium=partners&utm_source=linkshare&utm_campaign=SAyYsTvLiGQ

ML Subreddit:
https://www.reddit.com/r/machinelearning

Filecoin:
http://filecoin.io/

OpenMined:
http://openmined.org/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Daniels website:
http://www.danielrigberg.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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21.6 Andrew Trask - Really Quick Questions with an AI Researcher

http://www.youtube.com/watch?v=qJ1rdVEcl5g

I ask 67 questions to Oxford Scholar and AI researcher Andrew Trask as we go for a walk through Granary Square in London, England. Trask is a PhD student at Oxford University where he researches Deep Learning approaches with special emphasis on human language. We worked together on my Udacity deep learning nanodegree and I have a great deal of respect for his technical storytelling ability.

Please hit that subscribe button if you liked this interview!

And like. And comment. That's what keeps me going.

Some of Trask's work here:
https://www.manning.com/books/grokking-deep-learning
https://scholar.google.com/citations?user=2Ajxf1sAAAAJ&hl=en
https://iamtrask.github.io/

Here's his Linkedin:
https://www.linkedin.com/in/andrew-trask-545a6663/

Join other Wizards on our Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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21.7 Really Quick Questions with Siraj Raval

http://www.youtube.com/watch?v=XtHwjrm4sMg

I crowdsourced questions on Twitter, and picked 67 of them for an interview with myself! I had my friend Daniel Rigberg ask the questions and film me.

Please Subscribe! And like. And Comment. That's what keeps me going.

Resources I've mentioned:

Deep Learning Book:
http://www.deeplearningbook.org/

My playlists/courses:
https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A/playlists

Cormac McCarthy's Book:
https://www.amazon.com/Road-Cormac-McCarthy/dp/0307387895

Udacity Nanodegree:
https://www.udacity.com/course/deep-learning-nanodegree-foundation--nd101

Andrew Ng's Course:
https://www.coursera.org/specializations/deep-learning?siteID=SAyYsTvLiGQ-ZwaOIEdDzM4pwFY43MoUFQ&utm_content=10&utm_medium=partners&utm_source=linkshare&utm_campaign=SAyYsTvLiGQ

ML Subreddit:
https://www.reddit.com/r/machinelearning

Filecoin:
http://filecoin.io/

OpenMined:
http://openmined.org/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Daniels website:
http://www.danielrigberg.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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21.8 Stephen Merity - Really Quick Questions with a Salesforce Researcher

http://www.youtube.com/watch?v=nH39Sx5LX6Y

Stephen Merity is a senior research scientist working on deep learning in San Francisco with Salesforce Research via the MetaMind acquisition. He's been lucky enough to work with fascinating people and groups over the years including Google Sydney, Freelancer.com, the Schwa Lab at the University of Sydney, the team at Grok Learning, the non-profit Common Crawl, and IACS @ Harvard. I met him at Nvidia's GTC conference in San Jose and asked him 67 Questions about his life and thoughts on Machine Learning. Enjoy!

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Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
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instagram: https://www.instagram.com/sirajraval

More Learning resources:
https://www.linkedin.com/in/smerity/
https://twitter.com/Smerity
https://smerity.com/abme.html

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

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https://www.patreon.com/user?u=3191693
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21.9 Pieter Abbeel - Really Quick Questions with a Berkeley Professor

http://www.youtube.com/watch?v=KRFMM4duLHg

Dr Pieter Abbeel got his PhD at Stanford University under the mentorship of Andrew Ng and went on to become a professor at UC Berkeley. He's worked at OpenAI, Willow Garage and now Embodied Intelligence.Drawing on recent advances in Deep Imitation Learning and Deep Reinforcement Learning, Embodied Intelligence is developing AI software that makes it easy to teach robots new, complex skills. I caught him after he gave a lecture at Nvidia's GTC Conference in San Jose and asked him some really quick questions about his life and his thought on Machine Learning. Enjoy!

Please Subscribe! And like. And comment. That's what keeps me going.

Want more education? Connect with me here:
Twitter: https://twitter.com/sirajraval
Facebook: https://www.facebook.com/sirajology
instagram: https://www.instagram.com/sirajraval

More Learning resources:
https://people.eecs.berkeley.edu/~pabbeel/
https://twitter.com/pabbeel?lang=en
https://scholar.google.com/citations?user=vtwH6GkAAAAJ&hl=en

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http://wizards.herokuapp.com/

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22 Live Sessions

22.1 Build a Game Bot (LIVE)

http://www.youtube.com/watch?v=3vxk91K1PiI

This is my first live stream ever. I'm going to be using OpenAI's Gym library to build a bot that gets better and better at playing a 2D game like Pac-Man.I'll also just be live to answer any questions about me, my life, and whatever else you guys wanna talk about!

Code for this video:
https://github.com/llSourcell/build_a_game_bot_live

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

OpenAI Gym:
https://github.com/openai/gym

An article with in-depth explanations:
http://kvfrans.com/simple-algoritms-for-solving-cartpole/

Patreon:
https://www.patreon.com/user?u=3191693
Follow me:
Twitter: https://twitter.com/sirajraval
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22.2 Build a Neural Network (LIVE)

http://www.youtube.com/watch?v=KvoZU-ItDiE

In this video, I'll be building and training an LSTM Neural Network on a dataset of city names. Then it'll be able to generate new city names from scratch.

Code for this video:
https://github.com/llSourcell/build_a_neural_net_live/blob/master/README.md

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Read up more on TFLearn:
https://github.com/tflearn/tflearn

Incredible article on LSTMs:
http://colah.github.io/posts/2015-08-Understanding-LSTMs/

If you liked this stream, support me on Patreon! I do this full-time currently.
https://www.patreon.com/user?u=3191693
Follow me:
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22.3 Pong Neural Network (LIVE)

http://www.youtube.com/watch?v=Hqf__FlRlzg

In this video we're going to build the popular game Pong from scratch in Python, then train a neural network to become an unbeatable 2nd player! We use Tensorflow to build our neural net and pygame to build our Pong game.

The full, working code for this video is here:
https://github.com/llSourcell/pong_neural_network_live

Unlike my previous 2 live sessions where i did less than 60 lines of code each, I tried to do about 400 lines of code in this one. So I didn't have time to get to everything!

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Please share this video, like, comment and subscribe! And please support me on Patreon!:
https://www.patreon.com/user?u=3191693

That's what keeps me going. I love you all.
Follow me:
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22.4 Visualizing Data with D3.js (LIVE)

http://www.youtube.com/watch?v=sEpRzyPRH0s

In this session, I'm going to show you how to visualize data using the popular data visualization library (D3.js). This is useful for showing your results from machine learning algorithms, or just for you to understand what your data looks like. We visualize survivor stats from the popular Kaggle Titanic Survivor dataset.

The code in this video can be found here:
https://github.com/llSourcell/D3_Data_visualization_live

Please support me on Patreon! I want to continue to do this Youtube channel full-time:
https://www.patreon.com/user?u=3191693

Comment, Like, and Subscribe! That's what keeps me going. :)
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22.5 Build a Web Scraper (LIVE)

http://www.youtube.com/watch?v=A0Ac_dKNmH0

In this video, we'll build a Python web scraper that retrieves the top 20 most frequent words with their percentages in an English Wikipedia article.

Code for this video is here:
https://github.com/llSourcell/web_scraper_live_demo

Check out my friend Zoe Hong's Youtube channel for some cool fashion and illustration educational videos:
https://www.youtube.com/channel/UCMQ_mPIBPi4IMpYEmuyOMqQ

Please subscribe, comment, and like! That's what keeps me going.

2 more web scraping tutorials that are pretty good:

http://web.stanford.edu/~zlotnick/TextAsData/Web_Scraping_with_Beautiful_Soup.html

https://blog.miguelgrinberg.com/post/easy-web-scraping-with-python


Let me know of what types of things you'd like me to code in the future for live sessions, always open to suggestions. And please support me on Patreon!

https://www.patreon.com/user?u=3191693
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22.6 How to Make a Neural Network (LIVE)

http://www.youtube.com/watch?v=vcZub77WvFA

In this video, we're gonna make a neural network in Python from scratch!

Code for this video is here:
https://github.com/llSourcell/make_a_neural_net_live_demo

Join other Wizards on our Slack:
http://wizards.herokuapp.com/

More Neural Network resources:
http://pages.cs.wisc.edu/~bolo/shipyard/neural/local.html
https://www.quora.com/What-is-an-intuitive-explanation-for-neural-networks
http://karpathy.github.io/neuralnets/

Please Subscribe! That's the most important thing to me.

And support me on Patreon:
https://www.patreon.com/user?u=3191693
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22.7 How to Make a Tensorflow Neural Network (LIVE)

http://www.youtube.com/watch?v=qVwm-9P609I

In this live stream, we're going to use Tensorflow to build a convolutional neural network capable of classifying images. You'll need 'tensorflow' and the 'future' python libraries installed. The connection was laggy for the live stream and that won't happen again.

4:09-5:50 (The connection drops out)

The code for this video is here:
https://github.com/llSourcell/tensorflow_neural_net_live_demo/blob/master/README.md

More learning resources:
https://www.tensorflow.org/versions/r0.10/tutorials/mnist/beginners/index.html
http://deeplearning.net/tutorial/gettingstarted.html
http://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras/
https://www.oreilly.com/learning/not-another-mnist-tutorial-with-tensorflow?log-in
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22.8 How to Make a Path Planning Algorithm Easily (LIVE)

http://www.youtube.com/watch?v=2cQK_brSVvo

We're going to create a visual grid of squares with obstacles in it. Then, we'll use computer vision and a path planning algorithm to find the optimal route from point A to point B in the grid. You'll need the OpenCV, scikit-image, and numpy libraries installed for python.

Please subscribe! and like and comment. That's what keeps me going.

The code for this video is here:
https://github.com/llSourcell/path_planning_demo_live

Join us in our Slack channel:
http://wizards.herokuapp.com/

More learning resources:
https://www.youtube.com/watch?v=sAoBeujec74
https://www.raywenderlich.com/4946/introduction-to-a-pathfinding
http://docs.opencv.org/2.4/doc/tutorials/tutorials.html
https://www.udacity.com/course/introduction-to-computer-vision--ud810


and please support me on Patreon:
https://www.patreon.com/user?u=3191693
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22.9 How to Make a Tic Tac Toe Neural Network Easily (LIVE)

http://www.youtube.com/watch?v=0a-52ntK3T8

I'll use pure Javascript to build a neural network that evolves via a genetic algorithm to eventually become amazing at Tic Tac Toe!

Code for this video is here:
https://github.com/llSourcell/tic_tac_toe_neural_network-LIVE

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Some things i mentioned in the video ---

Free Harvard Neuroscience course:
https://www.mcb80x.org/

Great newer Deep Learning Books:
http://www.deeplearningbook.org/
http://www.mlyearning.org/

Also tweet Andrew Ng directly and ask him if he will interview with @sirajraval . I'm trying to get him on my 'Really Quick Questions' series.

Please subscribe, like, and comment! That's what keeps me going.

and please support me on Patreon:
https://www.patreon.com/user?u=3191693
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22.10 How to Make an Asteroids Game Bot (LIVE)

http://www.youtube.com/watch?v=h2qVYpK6TPE

In this video, we'll make a bot using a technique called 'Neuroevolution' to defeat the popular game of asteroids! This will be in Javascript. I talk about the architecture (neurons, layers, networks, genomes, generations) and the interesting actions we'll take to improve our bot (like breeding and mutation).

The code for this video is here:
https://github.com/llSourcell/asteroids_game_bot_LIVE

More learning resources:
http://eplex.cs.ucf.edu/hyperNEATpage/
http://stackoverflow.com/questions/31708478/how-to-evolve-weights-of-a-neural-network-in-neuroevolution
http://nn.cs.utexas.edu/?neuroevolution

Shoutout to Daniel Shiffman! This is apart of a collaboration with him:
https://www.youtube.com/watch?v=hacZU523FyM&t=94s

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And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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22.11 How to Install OpenAI's Universe and Make a Game Bot [LIVE]

http://www.youtube.com/watch?v=XI-I9i_GzIw

I'm going to go through the steps necessary to install OpenAI's Universe, then we'll build our own game bot using reinforcement learning. This code will be in Python.

*Update - I said something big was coming out this Friday in this video. I just got out of a meeting with the PR team i'm working with for promotion and they pleaded with me to release it next Friday (1/13) instead. So that''ll happen then. Just wanted to let you guys know.

Code for this video:
https://github.com/llSourcell/OpenAI_Game_Bot_Live_stream

Please Subscribe! And Like. And Comment. That's what keeps me going.

Some past submissions for the 'Make a Game Bot' Challenge using OpenAI's Universe':
https://github.com/av80r/coaster_racer_coding_challenge (winner)
https://github.com/rhnvrm/universe-coaster-racer-challenge

More Learning Resources:
https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0
https://www.youtube.com/watch?v=mGYU5t8MO7s
http://karpathy.github.io/2016/05/31/rl/
http://www.wildml.com/category/reinforcement-learning/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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22.12 How to do Object Detection with OpenCV [LIVE]

http://www.youtube.com/watch?v=OnWIYI6-4Ss

I'll be using OpenCV + Python to detect strawberries in an image. This will take about 45 minutes and it'll be less than 100 lines of code.

Code for this video is here:
https://github.com/llSourcell/Object_Detection_demo_LIVE

Please subscribe! And like. And comment. That's what keeps me going.

More learning resources:
http://docs.opencv.org/2.4/doc/tutorials/tutorials.html
https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_tutorials.html
https://www.youtube.com/watch?v=lJYEup-0gJo
https://realpython.com/blog/python/face-recognition-with-python/
https://gravityjack.com/news/opencv-python-3-homebrew/
http://www.simplecv.org/

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com

And please support me on Patreon!:
https://www.patreon.com/user?u=3191693
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22.13 Linear Regression Machine Learning (tutorial)

http://www.youtube.com/watch?v=uwwWVAgJBcM

I'll perform linear regression from scratch in Python using a method called 'Gradient Descent' to determine the relationship between student test scores & amount of hours studied. This will be about 50 lines of code and I'll deep dive into the math behind this.

Code for this video:
https://github.com/llSourcell/linear_regression_live

Please subscribe! And like. And comment. That's what keeps me going. And yes, this video is apart of my 'Intro to Deep Learning series'

More learning resources:
http://mathinsight.org/image/partial_derivative_as_slope
http://www.dummies.com/education/math/calculus/how-to-use-a-partial-derivative-to-measure-a-slope-in-three-dimensions/
https://spin.atomicobject.com/2014/06/24/gradient-descent-linear-regression/
https://www.quora.com/What-is-an-intuitive-explanation-of-gradient-descent
http://machinelearningmastery.com/gradient-descent-for-machine-learning/

Join us in the Wizards Slack Channel:
http://wizards.herokuapp.com/

Please support me on Patreon:
https://www.patreon.com/user?u=3191693
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22.14 How to Use Tensorflow for Classification (LIVE)

http://www.youtube.com/watch?v=4urPuRoT1sE

In this live session I'll introduce & give an overview of Google's Deep Learning library, Tensorflow. Then we'll use it to build a neural network capable of predicting housing prices, with me explaining every step along the way.

Code for this video:
https://github.com/llSourcell/How_to_use_Tensorflow_for_classification-LIVE

Please subscribe! And like. And comment. That's what keeps me going.

More learning resources:
https://jalammar.github.io/visual-interactive-guide-basics-neural-networks/
https://www.oreilly.com/learning/hello-tensorflow
https://www.tensorflow.org/tutorials/mnist/beginners/
https://github.com/aymericdamien/TensorFlow-Examples
https://www.youtube.com/watch?v=2FmcHiLCwTU&t=84s
https://cs224d.stanford.edu/lectures/CS224d-Lecture7.pdf

Join other Wizards on our Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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22.15 Word2Vec (tutorial)

http://www.youtube.com/watch?v=pY9EwZ02sXU

In this video, we'll use a Game of Thrones dataset to create word vectors. Then we'll map these word vectors out on a graph and use them to tell us related words that we input. We'll learn how to process a dataset from scratch, go over the word vectorization process, and visualization techniques all in one session.

Code for this video:
https://github.com/llSourcell/word_vectors_game_of_thrones-LIVE

Join us in our Slack channel:
http://wizards.herokuapp.com/

More learning resources:
https://www.tensorflow.org/tutorials/word2vec/
https://radimrehurek.com/gensim/models/word2vec.html
https://www.kaggle.com/c/word2vec-nlp-tutorial/details/part-1-for-beginners-bag-of-words
http://sebastianruder.com/word-embeddings-1/
http://natureofcode.com/book/chapter-1-vectors/

Please subscribe. And like. And Comment. That's what keeps me going.

And please support me on Patreon:
https://www.patreon.com/user?u=3191693
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22.16 How to Predict Music You Love (LIVE)

http://www.youtube.com/watch?v=18adykNGhHU

In this video, we're going to look at several different type of recommender systems in an iPython notebook. Popularity based, item-item collaborative, then user-item collaborative. Then we'll touch on the bleeding edge in deep learning at the end. Also I freestyle. Twice lol.

Code for this video:
https://github.com/llSourcell/recommender_live

More learning resources:
http://tech.hulu.com/blog/2016/08/01/cfnade.html
https://blogs.msdn.microsoft.com/carlnol/2012/06/23/co-occurrence-approach-to-an-item-based-recommender/
https://www.mapr.com/blog/inside-look-at-components-of-recommendation-engine
https://www.ics.uci.edu/~welling/teaching/CS77Bwinter12/presentations/course_Ricci/13-Item-to-Item-Matrix-CF.pdf
https://www.analyticsvidhya.com/blog/2016/06/quick-guide-build-recommendation-engine-python/
http://blogs.gartner.com/martin-kihn/how-to-build-a-recommender-system-in-python/

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22.17 How to Simplify Your Dataset Easily (LIVE)

http://www.youtube.com/watch?v=K796Ae4gLlY

We're going to compare some different techniques that reduce the dimensionality of our data so we can visualize it. We'll go through each one step by step including the math and I'll answer questions along the way. And I freestyle.

Code for this video:
https://github.com/llSourcell/How_to_Simplify_Your_Data-LIVE-

Links from the video:
https://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/
http://setosa.io/ev/eigenvectors-and-eigenvalues/

More learning resources:
https://plot.ly/ipython-notebooks/principal-component-analysis/
http://sebastianraschka.com/Articles/2014_pca_step_by_step.html
https://www.quora.com/What-is-the-difference-between-LDA-and-PCA-for-dimension-reduction
https://www.quora.com/What-advantages-the-t-sne-algorithm-has-over-pca
http://stats.stackexchange.com/questions/123040/whats-wrong-with-t-sne-vs-pca-for-dimensional-reduction-using-r
https://www.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm

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22.18 How to Make a Tensorflow Image Classifier (LIVE)

http://www.youtube.com/watch?v=APmF6qE3Vjc

We're going to build an image classifier using just Tensorflow (no Keras). This will be in depth, the goal for this video is for you to fully understand how a Convolutional Neural Network works. We'll visualize the filters we create along the way as well.

Code for this video: https://github.com/llSourcell/How_to_make_a_tensorflow_image_classifier_LIVE/blob/master/demonotes.ipynb

More CNN learning resources:
http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/
https://adeshpande3.github.io/adeshpande3.github.io/A-Beginner's-Guide-To-Understanding-Convolutional-Neural-Networks/
http://cs231n.github.io/convolutional-networks/
http://deeplearning.net/tutorial/lenet.html
http://neuralnetworksanddeeplearning.com/chap6.html
http://machinelearningmastery.com/crash-course-convolutional-neural-networks/
https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/

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22.19 How to Use Tensorflow for Time Series (Live)

http://www.youtube.com/watch?v=hhJIztWR_vo

We're going to use Tensorflow to predict the next event in a time series dataset. This can be applied to any kind of sequential data.

Code for this video:
https://github.com/llSourcell/rnn_tutorial

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More learning resources:
https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series
https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data
https://www.reddit.com/r/MachineLearning/comments/4ervmf/tensorflow_rnn_time_series_prediction/
https://danijar.com/introduction-to-recurrent-networks-in-tensorflow/
http://nbviewer.jupyter.org/github/jsseely/tensorflow-rnn-tutorial/blob/master/TensorFlow%20RNN%20tutorial.ipynb

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22.20 How to Do Style Transfer with Tensorflow (LIVE)

http://www.youtube.com/watch?v=YoBEGQD3LCc

We're going to learn about all the details of style transfer (especially the math) using just Tensorflow. The goal of this session is for you to understand the details behind how style+content loss is calculated and minimized. We'll also talk about future discoveries.

Code for this video:
https://github.com/llSourcell/How_to_do_style_transfer_in_tensorflow

Learning resources:
http://www.makeuseof.com/tag/create-neural-paintings-deepstyle-ubuntu/
https://blog.paperspace.com/art-with-neural-networks/
https://www.tensorflow.org/versions/r0.11/how_tos/
https://no2147483647.wordpress.com/2015/12/21/deep-learning-for-hackers-with-mxnet-2/
https://code.facebook.com/posts/196146247499076/delivering-real-time-ai-in-the-palm-of-your-hand/
http://kawahara.ca/deep-dreams-and-a-neural-algorithm-of-artistic-style-slides-and-explanations/
http://www.chioka.in/tensorflow-implementation-neural-algorithm-of-artistic-style

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22.21 How to Generate Music with Tensorflow (LIVE)

http://www.youtube.com/watch?v=pg9apmwf7og

This live session will focus on the details of music generation using the Tensorflow library. The goal is for you to understand the details of how to encode music, feed it to a well tuned model, and use it to generate really cool sounds. And I'm going to NOT use Google Hangouts, instead I'll do this with a green screen and a DSLR camera :)

Code for this video:
https://github.com/llSourcell/music_demo_live/

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My Udacity course is open for enrollments until this Saturday at midnight:
https://www.udacity.com/course/deep-learning-nanodegree-foundation--nd101

More Learning Resources:
http://www.asimovinstitute.org/analyzing-deep-learning-tools-music/
http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/
https://github.com/hexahedria/biaxial-rnn-music-composition
http://www.hexahedria.com/2016/08/08/summer-research-on-the-hmc-intelligent-music-software-team
https://magenta.tensorflow.org/
https://github.com/farizrahman4u/seq2seq
http://stackoverflow.com/questions/14448380/how-do-i-read-a-midi-file-change-its-instrument-and-write-it-back
https://github.com/vishnubob/python-midi

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22.22 How to Generate Your Own Wikipedia Articles (LIVE)

http://www.youtube.com/watch?v=ZGU5kIG7b2I

We're going to build an LSTM network in Tensorflow (no Keras) to generate text after training on Wikipedia articles. You'll learn how an LSTM cell works programmatically since we'll build one using TF's math functions and how you can parse a similar dataset

Code:
https://github.com/llSourcell/wiki_generator_live

Dataset: https://metamind.io/research/the-wikitext-long-term-dependency-language-modeling-dataset/


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Learning resources:
http://stats.stackexchange.com/questions/181/how-to-choose-the-number-of-hidden-layers-and-nodes-in-a-feedforward-neural-netw
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
https://github.com/jsseely/tensorflow-rnn-tutorial/blob/master/TensorFlow%20RNN%20tutorial.ipynb
https://chunml.github.io/ChunML.github.io/project/Creating-Text-Generator-Using-Recurrent-Neural-Network/
http://deeplearningathome.com/2016/10/Text-generation-using-deep-recurrent-neural-networks.html
https://larseidnes.com/2015/10/13/auto-generating-clickbait-with-recurrent-neural-networks/
http://genekogan.com/works/learning-sequences/

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https://www.patreon.com/user?u=3191693

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22.23 How to Use Tensorflow for Seq2seq Models (LIVE)

http://www.youtube.com/watch?v=ElmBrKyMXxs

Let's build a Sequence to Sequence model in Tensorflow to learn exactly how they work. You can use this model to make chatbots, language translators, text generators, and much more . We'll go over memory, attention, and some variants (like bidirectional layers) both programmatically and mathematically.

Code for this video:
https://github.com/llSourcell/seq2seq_model_live/blob/master/2-seq2seq-advanced.ipynb

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More Learning resources:
https://www.tensorflow.org/tutorials/seq2seq
http://www.kdnuggets.com/2015/06/rnn-tutorial-sequence-learning-recurrent-neural-networks.html
http://suriyadeepan.github.io/2016-06-28-easy-seq2seq/
https://indico.io/blog/sequence-modeling-neuralnets-part1/
http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/

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22.24 How to Use Tensorboard (LIVE)

http://www.youtube.com/watch?v=fBVEXKp4DIc

We're going to learn how the visualizer that comes with Tensorflow works in this live stream. We'll go through a bunch of different features and test out its functionality both programmatically and visually.

4:41 code begins
37:07 tensorboard visualization begins

Code for this video:
https://github.com/llSourcell/how_to_use_tensorboard_live/tree/master

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More Learning resources:
https://www.tensorflow.org/get_started/summaries_and_tensorboard
http://ischlag.github.io/2016/06/04/how-to-use-tensorboard/
https://www.youtube.com/watch?v=3bownM3L5zM
https://blog.altoros.com/visualizing-tensorflow-graphs-with-tensorboard.html
http://www.titiapps.com/hands-on-tensorboard-tensorflow-dev-summit-2017/

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22.25 How to Beat Pong Using Policy Gradients (LIVE)

http://www.youtube.com/watch?v=PDbXPBwOavc

We're going to use the policy gradient technique from reinforcement learning to beat the game of Pong. We'll use OpenAI's Universe as an environment for our agent and I'll go over the process of setting it up as well as the math behind the PG method in detail.

Microphone popping issues end at 11:15 . That cannot happen again. Udacity is aware of this and will be more prepared next time.

Code for this video:
https://github.com/llSourcell/Policy_Gradients_to_beat_Pong

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

More Learning resources:
http://www.scholarpedia.org/article/Policy_gradient_methods
http://proceedings.mlr.press/v32/silver14.pdf
http://karpathy.github.io/2016/05/31/rl/
http://home.deib.polimi.it/restelli/MyWebSite/pdf/rl7.pdf
http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching_files/pg.pdf
https://github.com/dennybritz/reinforcement-learning/tree/master/PolicyGradient

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22.26 How to Generate Images with Tensorflow (LIVE)

http://www.youtube.com/watch?v=iz-TZOEKXzA

We'll build a Variational Autoencoder using Tensorflow to generate images. We'll go through several examples including digit images and pokemon images. VAE's allow us to generate, compress, denoise, and even fuse images together. They are an incredibly powerful tool and we'll go over the implementation details (math included) in this live session.

Code: https://github.com/llSourcell/how_to_generate_images_with_tensorflow_LIVE

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More Learning resources:
https://arxiv.org/abs/1606.05908
https://github.com/stitchfix/fauxtograph
http://deeplearning.jp/cvae/
https://ift6266h17.wordpress.com/2017/03/26/q3-reparameterization-trick-of-variational-autoencoder/
https://www.quora.com/What-is-the-latent-loss-in-variational-autoencoders
https://www.slideshare.net/ShaiHarel/variational-autoencoder-talk

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22.27 Generative Adversarial Networks (LIVE)

http://www.youtube.com/watch?v=0VPQHbMvGzg

We're going to build a GAN to generate some images using Tensorflow. This will help you grasp the architecture and intuition behind adversarial approaches to machine learning. We're building a Deep Convolutional GAN to generate MNIST digits.

Code for this video:
https://github.com/llSourcell/Generative_Adversarial_networks_LIVE/blob/master/EZGAN.ipynb

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More Learning resources:
http://guimperarnau.com/blog/2017/03/Fantastic-GANs-and-where-to-find-them
http://www.cs.toronto.edu/~dtarlow/pos14/talks/goodfellow.pdf
https://datawarrior.wordpress.com/2017/02/03/generative-adversarial-networks/
https://www.quora.com/What-are-Generative-Adversarial-Networks
http://nuit-blanche.blogspot.com/2017/01/nips-2016-tutorial-generative.html
http://www.paddlepaddle.org/develop/doc/tutorials/gan/index_en.html
http://gkalliatakis.com/blog/delving-deep-into-gans

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22.28 Generative Adversarial Networks for Style Transfer (LIVE)

http://www.youtube.com/watch?v=MgdAe-T8obE

Generative Adversarial Nets are such a rich topic for exploration, we're going to build one that was released just 2 months ago called the "DiscoGAN" that lets us transfer the style between 2 datasets. And I'll be building this using Tensorflow.

Code for this video:
https://github.com/llSourcell/GANS-for-style-transfer

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More learning resources:
https://arxiv.org/abs/1703.05192
https://github.com/SKTBrain/DiscoGAN
https://www.reddit.com/r/MachineLearning/comments/5zp0eu/r_170305192_learning_to_discover_crossdomain/
https://medium.com/@ageitgey/abusing-generative-adversarial-networks-to-make-8-bit-pixel-art-e45d9b96cee7

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22.29 Differentiable Neural Computer (LIVE)

http://www.youtube.com/watch?v=r5XKzjTFCZQ

The Differentiable Neural Computer is an awesome model that DeepMind recently released. It's a memory augmented network that can perform meta-learning (learning to learn). We'll go over it's architecture details and implement it ourselves in Tensorflow.

Code for this video: https://github.com/llSourcell/differentiable_neural_computer

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More learning resources:
https://deepmind.com/blog/differentiable-neural-computers/
https://www.quora.com/How-groundbreaking-is-DeepMinds-Differentiable-neural-network
https://github.com/dsindex/blog/wiki/%5Bdnc%5D-Differentiable-Neural-Computer
https://blog.acolyer.org/2016/03/09/neural-turing-machines/
https://thenewstack.io/googles-deepmind-ai-now-capable-deep-neural-reasoning/

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23 Neural Networks

23.1 Build a Neural Net in 4 Minutes

http://www.youtube.com/watch?v=h3l4qz76JhQ

How does a Neural network work? Its the basis of deep learning and the reason why image recognition, chatbots, self driving cars, and language translation work! In this video, i'll use python to code up a neural network in just 4 minutes using just the numpy library, capable of doing matrix mathematics.

Code for this video:
https://github.com/llSourcell/Make_a_neural_network

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2 Great Neural Net Tutorials:

(please subscribe for more videos like these! )

1. https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1#.l51z38s7f

2. https://iamtrask.github.io/2015/07/12/basic-python-network/

Awesome Tutorial Series on Neural Networks:

http://lumiverse.io/series/neural-networks-demystified

The Canonical Machine Learning Course:

https://www.coursera.org/learn/machine-learning

Curious just how inspired neural networks are from brain architecture? Take some time to learn about the human brain! This is my favorite intro to neuroscience course:

https://www.mcb80x.org/
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23.2 How to Make a Neural Network (LIVE)

http://www.youtube.com/watch?v=vcZub77WvFA

In this video, we're gonna make a neural network in Python from scratch!

Code for this video is here:
https://github.com/llSourcell/make_a_neural_net_live_demo

Join other Wizards on our Slack:
http://wizards.herokuapp.com/

More Neural Network resources:
http://pages.cs.wisc.edu/~bolo/shipyard/neural/local.html
https://www.quora.com/What-is-an-intuitive-explanation-for-neural-networks
http://karpathy.github.io/neuralnets/

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23.3 Build an Autoencoder in 5 Min - Fresh Machine Learning #5

http://www.youtube.com/watch?v=GWn7vD2Ud3M

This video is all about autoencoders! I start off explaining what an autoencoder is and how it works. Then I talk about some use cases for autoencoders and the special types of autoencoders we use for each of them. Finally, I programmatically go through an example of a simple autoencoder, followed by a demo.

The code for this video is here:

https://github.com/llSourcell/autoencoder_demo/tree/master

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Autoencoder live demo in the browser:

https://cs.stanford.edu/people/karpathy/convnetjs/demo/autoencoder.html

and here are some great links to read up on autoencoders:

http://ufldl.stanford.edu/tutorial/unsupervised/Autoencoders/

http://lazyprogrammer.me/a-tutorial-on-autoencoders/

https://blog.keras.io/building-autoencoders-in-keras.html

https://www.quora.com/What-are-the-best-resources-for-learning-about-autoencoders-from-scratch

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

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23.4 Build a Neural Network (LIVE)

http://www.youtube.com/watch?v=KvoZU-ItDiE

In this video, I'll be building and training an LSTM Neural Network on a dataset of city names. Then it'll be able to generate new city names from scratch.

Code for this video:
https://github.com/llSourcell/build_a_neural_net_live/blob/master/README.md

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Read up more on TFLearn:
https://github.com/tflearn/tflearn

Incredible article on LSTMs:
http://colah.github.io/posts/2015-08-Understanding-LSTMs/

If you liked this stream, support me on Patreon! I do this full-time currently.
https://www.patreon.com/user?u=3191693
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23.5 Build a Recurrent Neural Net in 5 Min

http://www.youtube.com/watch?v=cdLUzrjnlr4

In this video, I explain the basics of recurrent neural networks. Then we code our own RNN in 80 lines of python (plus white-space) that predicts the sum of two binary numbers after training.

Code for this video:

https://github.com/llSourcell/recurrent_neural_net_demo

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Thank @iamtrask for a great RNN article:
https://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/

and this piece by Karpathy on RNN's deserves some sort of award:
http://karpathy.github.io/2015/05/21/rnn-effectiveness/

Another great RNN article:
http://nikhilbuduma.com/2015/01/11/a-deep-dive-into-recurrent-neural-networks/

Tensorflow RNNs:
https://www.tensorflow.org/versions/r0.10/tutorials/recurrent/index.html

Thanks so much for watching my videos, I do this stuff for you guys. I'm about to hit 10K subscribers soon. If and when I do, I'm going to start working on a machine learning music video to celebrate! With better production quality than my last two!

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment.
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23.6 Pong Neural Network (LIVE)

http://www.youtube.com/watch?v=Hqf__FlRlzg

In this video we're going to build the popular game Pong from scratch in Python, then train a neural network to become an unbeatable 2nd player! We use Tensorflow to build our neural net and pygame to build our Pong game.

The full, working code for this video is here:
https://github.com/llSourcell/pong_neural_network_live

Unlike my previous 2 live sessions where i did less than 60 lines of code each, I tried to do about 400 lines of code in this one. So I didn't have time to get to everything!

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Please share this video, like, comment and subscribe! And please support me on Patreon!:
https://www.patreon.com/user?u=3191693

That's what keeps me going. I love you all.
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23.7 Neural Networks - The Math of Intelligence #4

http://www.youtube.com/watch?v=ov_RkIJptwE

Have you ever wondered what the math behind neural networks looks like? What gives them such incredible power? We're going to cover 4 different neural networks in this video to develop an intuition around their basic principles (2 feedforward networks, 1 recurrent network, and a self-organizing map). Prepare yourself, deep learning is coming.

Code for this video (with coding challenge):
https://github.com/llSourcell/neural_networks

Hammad's winning code:
https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Regularization%20in%20Linear%20Regression

Ong's runner-up code:
https://github.com/jrios6/Math-of-Intelligence/tree/master/3-Regularization

More learning resources:
https://www.youtube.com/watch?v=h3l4qz76JhQ
http://www.ai-junkie.com/ann/som/som1.html
http://iamtrask.github.io/2015/07/12/basic-python-network/
https://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/
http://karpathy.github.io/2015/05/21/rnn-effectiveness/
https://www.youtube.com/watch?v=vOppzHpvTiQ&list=PL2-dafEMk2A7YdKv4XfKpfbTH5z6rEEj3

Please subscribe! And like. And comment. That's what keeps me going.

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23.8 The Evolution of Gradient Descent

http://www.youtube.com/watch?v=nhqo0u1a6fw

Which optimizer should we use to train our neural network? Tensorflow gives us lots of options, and there are way too many acronyms. We'll go over how the most popular ones work and in the process see how gradient descent has evolved over the years.

Code from this video (with coding challenge):
https://github.com/llSourcell/The_evolution_of_gradient_descent/

Please subscribe! And like. And comment. Thats what keeps me going.

More learning resources:
http://sebastianruder.com/optimizing-gradient-descent/
https://www.tensorflow.org/api_docs/python/tf/train/GradientDescentOptimizer
http://machinelearningmastery.com/gradient-descent-for-machine-learning/
http://cs231n.github.io/optimization-1/
https://www.cs.toronto.edu/~hinton/csc2515/notes/lec6tutorial.pdf
https://www.youtube.com/watch?v=umAeJ7LMCfU

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23.9 Which Activation Function Should I Use?

http://www.youtube.com/watch?v=-7scQpJT7uo

All neural networks use activation functions, but the reasons behind using them are never clear! Let's discuss what activation functions are, when they should be used, and what the difference between them is.

Sample code from this video:
https://github.com/llSourcell/Which-Activation-Function-Should-I-Use

Please subscribe! And like. And comment. That's what keeps me going.

More Learning resources:
http://www.kdnuggets.com/2016/08/role-activation-function-neural-network.html
http://cs231n.github.io/neural-networks-1/
https://www.quora.com/What-is-the-role-of-the-activation-function-in-a-neural-network
https://stats.stackexchange.com/questions/115258/comprehensive-list-of-activation-functions-in-neural-networks-with-pros-cons
https://en.wikibooks.org/wiki/Artificial_Neural_Networks/Activation_Functions
https://stackoverflow.com/questions/9782071/why-must-a-nonlinear-activation-function-be-used-in-a-backpropagation-neural-net
https://papers.nips.cc/paper/874-how-to-choose-an-activation-function.pdf
http://neuralnetworksanddeeplearning.com/chap4.html
https://medium.com/towards-data-science/activation-functions-in-neural-networks-58115cda9c96
https://medium.com/autonomous-agents/mathematical-foundation-for-activation-functions-in-artificial-neural-networks-a51c9dd7c089

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http://wizards.herokuapp.com/

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24 Intro to Tensorflow

24.1 TensorFlow in 5 Minutes (tutorial)

http://www.youtube.com/watch?v=2FmcHiLCwTU

This video is all about building a handwritten digit image classifier in Python in under 40 lines of code (not including spaces and comments). We'll use the popular library TensorFlow to do this.

Please subscribe! That would make me the happiest, and encourage me to output similar content.

The source code for this video is here:
https://github.com/llSourcell/tensorflow_demo

Here are some great links on TensorFlow:

Tensorflow setup: https://www.tensorflow.org/versions/r0.10/get_started/os_setup.html#pip-installation

A similar written tutorial by Google:
https://www.tensorflow.org/versions/r0.9/tutorials/mnist/beginners/index.html

Tensorflow Course:
https://www.udacity.com/course/deep-learning--ud730

Awesome intro to Tensorflow:
https://www.oreilly.com/learning/hello-tensorflow

Some other great introductory examples using Tensorflow:
https://github.com/aymericdamien/TensorFlow-Examples

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
https://www.patreon.com/user?ty=h&u=3191693
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24.2 Deep Learning Frameworks Compared

http://www.youtube.com/watch?v=MDP9FfsNx60

In this video, I compare 5 of the most popular deep learning frameworks (SciKit Learn, TensorFlow, Theano, Keras, and Caffe). We go through the pros and cons of each, as well as some code samples, eventually coming to a definitive conclusion.

The code for the TensorFlow vs Theano part of the video is here:
https://github.com/llSourcell/tensorflow_vs_theano

An article that explains the differences in more detail:
https://medium.com/@sentimentron/faceoff-theano-vs-tensorflow-e25648c31800#.bg4xmz1au

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Learn more about TF Learn here:
https://github.com/tflearn/tflearn

and here:
https://www.tensorflow.org/versions/r0.9/tutorials/tflearn/index.html

Learn more about TensorFlow here:
https://www.oreilly.com/learning/hello-tensorflow

More on Keras here:
http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/

More on SciKit Learn here:
http://scikit-learn.org/stable/tutorial/

More on Caffe here:
http://christopher5106.github.io/deep/learning/2015/09/04/Deep-learning-tutorial-on-Caffe-Technology.html

More on Theano here:
https://github.com/Newmu/Theano-Tutorials

Thanks for watching guys, I do this for you. If you like my videos, feel free to support me on Patreon and please LIKE, SUBSCRIBE, COMMENT, AND SHARE!

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24.3 Generate Music in TensorFlow

http://www.youtube.com/watch?v=ZE7qWXX05T0

In this video, I go over some of the state of the art advances in music generation coming out of DeepMind. Then we build our own music generation script in Python using Tensorflow and a type of neural network called a Restricted Boltzmann Machine. Congrats to Rohan Verma (Winner) and Chih-Cheng Liang (runner-up) for their classifiers for scientists. The challenge for this video is to generate a happy/upbeat song using the RBM Script.

The code for this video is here:
https://github.com/llSourcell/Music_Generator_Demo

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

The WaveNet blogpost with audio samples:
https://deepmind.com/blog/wavenet-generative-model-raw-audio/

More on RBMs:
http://deeplearning4j.org/restrictedboltzmannmachine.html

Another write up on music generation with Neural Networks:
http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/

Interesting Machine Music Generation Project by Google:
https://magenta.tensorflow.org/welcome-to-magenta

TensorFlow course on Udacity:
https://www.udacity.com/course/deep-learning--ud730

Rohan's Classifier (Winner):
https://github.com/rhnvrm/galaxy-image-classifier-tensorflow

Chih-Cheng's Classifier (Runner-up):
https://github.com/ChihChengLiang/tensorflow-night-heron-classifier

Please subscribe, like, and comment! You guys are the reason I do this. Thanks so much for watching my videos! If you enjoy my videos, I'd appreciate your support on Patreon :)

https://www.patreon.com/user?u=3191693
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24.4 The Best Way to Prepare a Dataset Easily

http://www.youtube.com/watch?v=0xVqLJe9_CY

In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it). The example I use is preparing a dataset of brain scans to classify whether or not someone is meditating.

The challenge for this video is here:
https://github.com/llSourcell/prepare_dataset_challenge

Carl's winning code:
https://github.com/av80r/coaster_racer_coding_challenge

Rohan's runner-up code:
https://github.com/rhnvrm/universe-coaster-racer-challenge

Come join other Wizards in our Slack channel:
http://wizards.herokuapp.com/

Dataset sources I talked about:
https://github.com/caesar0301/awesome-public-datasets
https://www.kaggle.com/datasets
http://reddit.com/r/datasets

More learning resources:
https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-prepare-data
http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/
https://www.youtube.com/watch?v=kSslGdST2Ms
http://freecontent.manning.com/real-world-machine-learning-pre-processing-data-for-modeling/
http://docs.aws.amazon.com/machine-learning/latest/dg/step-1-download-edit-and-upload-data.html
http://paginas.fe.up.pt/~ec/files_1112/week_03_Data_Preparation.pdf

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24.5 Build a TensorFlow Image Classifier in 5 Min

http://www.youtube.com/watch?v=QfNvhPx5Px8

In this episode we're going to train our own image classifier to detect Darth Vader images.

The code for this repository is here:
https://github.com/llSourcell/tensorflow_image_classifier

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

The Challenge:
The challenge for this episode is to create your own Image Classifier that would be a useful tool for scientists. Just post a clone of this repo that includes your retrained Inception Model (label it output_graph.pb). If it's too big for GitHub, just upload it to DropBox and post the link in your GitHub README. I'm going to judge all of them and the winner gets a shoutout from me in a future video, as well as a signed copy of my book 'Decentralized Applications'.

This CodeLab by Google is super useful in learning this stuff:

https://codelabs.developers.google.com/codelabs/tensorflow-for-poets/?utm_campaign=chrome_series_machinelearning_063016&utm_source=gdev&utm_medium=yt-desc#0

This Tutorial by Google is also very useful:

https://www.tensorflow.org/versions/r0.9/how_tos/image_retraining/index.html

This is a good informational video:

https://www.youtube.com/watch?v=VpDonQAKtE4

Really deep dive video on CNNs:

https://www.youtube.com/watch?v=FmpDIaiMIeA

I love you guys! Thanks for watching my videos and if you've found any of them useful I'd love your support on Patreon:

https://www.patreon.com/user?u=3191693

Much more to come so please SUBSCRIBE, LIKE, and COMMENT! :)

edit: Credit to Clarifai for the first conv net diagram in the video
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24.6 Build a Neural Network (LIVE)

http://www.youtube.com/watch?v=KvoZU-ItDiE

In this video, I'll be building and training an LSTM Neural Network on a dataset of city names. Then it'll be able to generate new city names from scratch.

Code for this video:
https://github.com/llSourcell/build_a_neural_net_live/blob/master/README.md

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Read up more on TFLearn:
https://github.com/tflearn/tflearn

Incredible article on LSTMs:
http://colah.github.io/posts/2015-08-Understanding-LSTMs/

If you liked this stream, support me on Patreon! I do this full-time currently.
https://www.patreon.com/user?u=3191693
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24.7 Tensorboard Explained in 5 Min

http://www.youtube.com/watch?v=3bownM3L5zM

In this video, we first go through the code for a simple handwritten character classifier in Python, then visualize it in Tensorboard. The point of this video was to showcase Tensorboard as a data visualization tool. We also use a more complex handwritten character classifier to further showcase all of Tensorboard's features. This was the hardest video I've ever had to make in terms of timing. It was really difficult to fit this many TB features into this time frame.

The code for this video is here:

https://github.com/llSourcell/Tensorboard_demo

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Challenge:

The challenge for this video is to visualize some audio data in Tensorboard. To date, I haven't seen any repos on GitHub that do this. The audio feature seems relatively new in Tensorboard. The first person who does this and posts it in the comments by Sept 30 2016 gets a shoutout from me in my video release on that date!

Tensorboard tutorial:

https://www.tensorflow.org/versions/r0.7/how_tos/summaries_and_tensorboard/index.html

Another good TB tutorial:

https://www.tensorflow.org/versions/r0.7/how_tos/graph_viz/index.html

An unofficial tutorial:

http://www.slideshare.net/hunkim/tensor-board

A video i found on tensorboard:

https://www.youtube.com/watch?v=zp5EtBvwQbw

I love you guys! Thanks for watching my videos and if you've found any of them useful I'd love your support on Patreon:

https://www.patreon.com/user?u=3191693

Much more to come so please SUBSCRIBE, LIKE, and COMMENT! :)
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24.8 Deep Dream in TensorFlow - Learn Python for Data Science #5

http://www.youtube.com/watch?v=MrBzgvUNr4w

In this video, we replicate Google's Deep Dream code in 80 lines of Python using the Tensorflow machine learning library. Then we visualize it at the end.

The challenge for this video is here:
https://github.com/llSourcell/deep_dream_challenge

Avhirup's winning stock prediction code:
https://github.com/Avhirup/Stock-Market-Prediction-Challenge

Victor's runner-up code:
https://github.com/ciurana2016/predict_stock_py

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

More Deep Dream tutorials:

http://www.alanzucconi.com/2016/05/25/generating-deep-dreams/
https://github.com/awanninger/deepdream
http://ryankennedy.io/running-the-deep-dream/

Generate Deep Dream's online:
http://deepdreamgenerator.com/generator-style

Still my favorite intro to neuroscience class:
https://www.mcb80x.org/

Please subscribe! And share this video, like + comment. That's what keeps me going.

Please support me on Patreon if you like my videos:
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24.9 How to Make a Simple Tensorflow Speech Recognizer

http://www.youtube.com/watch?v=u9FPqkuoEJ8

In this video, we'll make a super simple speech recognizer in 20 lines of Python using the Tensorflow machine learning library. I go over the history of speech recognition research, then explain (and rap about) how we can build our own speech recognition system using the power of deep learning.

The code for this video is here:
https://github.com/llSourcell/tensorflow_speech_recognition_demo

Mick's winning code:
https://github.com/mickvanhulst/tf_chatbot_lotr

The weekly challenge can be found at the end of the 'Make a Game Bot' video:
https://www.youtube.com/watch?v=mGYU5t8MO7s

More learning resources:
https://www.superlectures.com/iscslp2014/tutorial-4-deep-learning-for-speech-generation-and-synthesis
http://andrew.gibiansky.com/blog/machine-learning/speech-recognition-neural-networks/
https://www.youtube.com/watch?v=LFDU2GX4AqM
https://www.youtube.com/watch?v=g-sndkf7mCs

Please subscribe! And like and comment. That's what keeps me going.

And please support me on Patreon! I don't work for anyone, although I did make a one-off video for OpenAI because I love them:
https://www.patreon.com/user?u=3191693
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24.10 How to Make a Tensorflow Neural Network (LIVE)

http://www.youtube.com/watch?v=qVwm-9P609I

In this live stream, we're going to use Tensorflow to build a convolutional neural network capable of classifying images. You'll need 'tensorflow' and the 'future' python libraries installed. The connection was laggy for the live stream and that won't happen again.

4:09-5:50 (The connection drops out)

The code for this video is here:
https://github.com/llSourcell/tensorflow_neural_net_live_demo/blob/master/README.md

More learning resources:
https://www.tensorflow.org/versions/r0.10/tutorials/mnist/beginners/index.html
http://deeplearning.net/tutorial/gettingstarted.html
http://machinelearningmastery.com/handwritten-digit-recognition-using-convolutional-neural-networks-python-keras/
https://www.oreilly.com/learning/not-another-mnist-tutorial-with-tensorflow?log-in
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24.11 How to Make an Amazing Tensorflow Chatbot Easily

http://www.youtube.com/watch?v=SJDEOWLHYVo

We'll go over how chatbots have evolved over the years and how Deep Learning has made them way better. Then we'll build our own chatbot using the Tensorflow machine learning library in Python.

The code & coding challenge for this video are here:
https://github.com/llSourcell/tensorflow_chatbot

Georgi's winning code for this week:
https://github.com/petkofff/p_vs_np_challenge

Mick's Runner up code for this week:
https://github.com/mickvanhulst/travSalesman

Join other Wizards on our Slack room:
https://wizards.herokuapp.com

Live sequence to sequence chatbot demo:
http://neuralconvo.huggingface.co/

Some more useful resources on chatbots:
http://www.wildml.com/2016/04/deep-learning-for-chatbots-part-1-introduction/
http://venturebeat.com/2016/08/01/how-deep-reinforcement-learning-can-help-chatbots/
http://web.stanford.edu/class/cs124/lec/chatbot.pdf

More resources on Tensorflow:
http://lauragelston.ghost.io/speakeasy-pt2/
https://speakerdeck.com/inureyes/building-ai-chat-bot-using-python-3-and-tensorflow

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25 Learn Python for Data Science

25.1 Introduction - Learn Python for Data Science #1

http://www.youtube.com/watch?v=T5pRlIbr6gg

Welcome to the 1st Episode of Learn Python for Data Science! This series will teach you Python and Data Science at the same time! In this video we install Python and our text editor (Sublime Text), then build a gender classifier using the sci-kit learn library in just about 10 lines of code.

Please subscribe & share this video if you liked it!

The code for this video is here:
https://github.com/llSourcell/gender_classification_challenge

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Download Python here:
https://www.python.org/downloads/

Download Sublime Text here:
https://www.sublimetext.com/3

Some Great simple sci-kit learn examples here:
https://github.com/chribsen/simple-machine-learning-examples

and the official scikit website:
http://scikit-learn.org/

Highly recommend this online book as supplementary reading material:
https://learnpythonthehardway.org/book/

Wondering when to use which model? This chart helps, but keep in mind deep neural nets outperform pretty much any model given enough data and computing power. so use these when you don't have access to loads of data and compute:
http://scikit-learn.org/stable/tutorial/machine_learning_map/

Thank you guys for watching! Subscribe, like, and comment! That's what keeps me going. Feel free to support me on Patreon:

https://www.patreon.com/user?u=3191693
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25.2 Twitter Sentiment Analysis - Learn Python for Data Science #2

http://www.youtube.com/watch?v=o_OZdbCzHUA

In this video we'll be building our own Twitter Sentiment Analyzer in just 14 lines of Python. It will be able to search twitter for a list of tweets about any topic we want, then analyze each tweet to see how positive or negative it's emotion is.

The coding challenge for this video is here:
https://github.com/llSourcell/twitter_sentiment_challenge

Naresh's winning code from last episode:
https://github.com/Naresh1318/GenderClassifier/blob/master/Run_Code.py

Victor's Runner up code from last episode:
https://github.com/Victor-Mazzei/ml-gender-python/blob/master/gender.py

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

More on TextBlob:
https://textblob.readthedocs.io/en/dev/

Great info on Sentiment Analysis:
https://www.quora.com/How-does-sentiment-analysis-work

Great sentiment analysis api:
http://www.alchemyapi.com/products/alchemylanguage/sentiment-analysis

Read over these course notes if you wanna become an NLP god:
http://cs224d.stanford.edu/syllabus.html

Best book to become a Python god:
https://learnpythonthehardway.org/

Please share this video, like, comment and subscribe!
That's what keeps me going.

Feel free to support me on Patreon:
https://www.patreon.com/user?u=3191693

Two Minute Papers Link:
https://www.youtube.com/playlist?list=PLujxSBD-JXgnqDD1n-V30pKtp6Q886x7e
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25.3 Recommendation Systems - Learn Python for Data Science #3

http://www.youtube.com/watch?v=9gBC9R-msAk

In this video, we build our own recommendation system that suggests movies a user would like in 40 lines of Python using the LightFM recommendation library. I start off by talking about why we need recommendation systems, then we dive straight into installing our dependencies and writing our script.

The coding challenge for this video is here:

https://github.com/llSourcell/recommender_system_challenge

The winner of last weeks coding challenge (Rohan Verma):
https://twitter-sentiment-csv.herokuapp.com/
https://t.co/4eg8UdlaSB

The runner up (Arnaud Delauney):
https://github.com/arnauddelaunay/twitter_sentiment_challenge

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

The LightFM Python Library:
https://github.com/lyst/lightfm/tree/master/lightfm

Some great learning resources on recommender systems:

http://blogs.gartner.com/martin-kihn/how-to-build-a-recommender-system-in-python/

https://www.analyticsvidhya.com/blog/2015/08/beginners-guide-learn-content-based-recommender-systems/

http://www.quuxlabs.com/blog/2010/09/matrix-factorization-a-simple-tutorial-and-implementation-in-python/

http://blog.manugarri.com/a-short-introduction-to-recommendation-systems/

Best book to become a Python God:
https://learnpythonthehardway.org/

Please share this video, like, comment and subscribe! That's what keeps me going.

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25.4 Predicting Stock Prices - Learn Python for Data Science #4

http://www.youtube.com/watch?v=SSu00IRRraY

In this video, we build an Apple Stock Prediction script in 40 lines of Python using the scikit-learn library and plot the graph using the matplotlib library.

The challenge for this video is here:
https://github.com/llSourcell/predicting_stock_prices

Victor's winning recommender code:
https://github.com/ciurana2016/recommender_system_py

Kevin's runner-up code:
https://github.com/Krewn/learner/blob/master/FieldPredictor.py#L62

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Stock prediction with Tensorflow:
https://nicholastsmith.wordpress.com/2016/04/20/stock-market-prediction-using-multi-layer-perceptrons-with-tensorflow/

Another great stock prediction tutorial:
http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/

This guy made 500K doing ML stuff with stocks:
http://jspauld.com/post/35126549635/how-i-made-500k-with-machine-learning-and-hft

Please share this video, like, comment and subscribe! That's what keeps me going.

and please support me on Patreon!:
https://www.patreon.com/user?u=3191693

Check out this youtube channel for some more cool Python tutorials:
https://www.youtube.com/watch?v=RZF17FfRIIo
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25.5 Deep Dream in TensorFlow - Learn Python for Data Science #5

http://www.youtube.com/watch?v=MrBzgvUNr4w

In this video, we replicate Google's Deep Dream code in 80 lines of Python using the Tensorflow machine learning library. Then we visualize it at the end.

The challenge for this video is here:
https://github.com/llSourcell/deep_dream_challenge

Avhirup's winning stock prediction code:
https://github.com/Avhirup/Stock-Market-Prediction-Challenge

Victor's runner-up code:
https://github.com/ciurana2016/predict_stock_py

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

More Deep Dream tutorials:

http://www.alanzucconi.com/2016/05/25/generating-deep-dreams/
https://github.com/awanninger/deepdream
http://ryankennedy.io/running-the-deep-dream/

Generate Deep Dream's online:
http://deepdreamgenerator.com/generator-style

Still my favorite intro to neuroscience class:
https://www.mcb80x.org/

Please subscribe! And share this video, like + comment. That's what keeps me going.

Please support me on Patreon if you like my videos:
https://www.patreon.com/user?u=3191693
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25.6 Genetic Algorithms - Learn Python for Data Science #6

http://www.youtube.com/watch?v=dSofAXnnFrY

In this video, we build a Gamma Radiation Classifier and use Genetic Programming to pick the best Machine Learning model + hyper-parameters FOR US in 40 lines of Python.

Challenge for this video:
https://github.com/llSourcell/genetic_algorithm_challenge

Peter's winning code:
https://github.com/PeterMitrano/deep_dream_challenge

Kyle's Runner up code:
https://github.com/ljlabs/deep_dream_challenge/blob/master/Dream_in_video.py

Great chapter on Genetic Algorithms:
http://natureofcode.com/book/chapter-9-the-evolution-of-code/

Link to TPOT:
https://github.com/rhiever/tpot

Join the Wizards Slack Channel:
https://wizards.herokuapp.com/

Please like + subscribe + comment!

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26 Fresh Machine Learning

26.1 One-Shot Learning - Fresh Machine Learning #1

http://www.youtube.com/watch?v=FIjy3lV_KJU

Welcome to Fresh Machine Learning! This is my new course dedicated to making bleeding edge machine learning accessible to developers everywhere.

The demo code for this video is a handwritten character classifier in Python using a One-Shot Learning technique with SciPy:

https://github.com/llSourcell/One-Shot-Learning-Demo

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

I introduce two papers in this video

Human Level Concept Learning through Probabilistic Program Induction:

http://web.mit.edu/cocosci/Papers/Science-2015-Lake-1332-8.pdf

and it's associated code is in MatLab:

https://github.com/brendenlake/BPL

but Matlab requires $ to download and Python is better suited for building production apps. I found a great alternative though, these guys are really close to finishing this python library. It's called 'PyBPL' they are working on making the results from the paper generalized so that you can apply BPL to any kind of example dataset. I talked with the lead dev and he said they'd have working demos out in 3-6 weeks so follow this repo!!:

https://github.com/MaxwellRebo/PyBPL

One-Shot Learning with Memory Augmented Neural Networks:

https://arxiv.org/pdf/1605.06065v1.pdf

and it's associated code is here:

https://github.com/tristandeleu/ntm-one-shot

Also, here's another very recent One Shot Learning Paper from DeepMind that I couldn't squeeze into this video but is very interesting:

https://arxiv.org/pdf/1606.04080v1.pdf

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment.
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26.2 Generative Adversarial Nets - Fresh Machine Learning #2

http://www.youtube.com/watch?v=deyOX6Mt_As

This episode of Fresh Machine Learning is all about a relatively new concept called a Generative Adversarial Network. A model continuously tries to fool another model, until it can do so with ease. At that point, it can generate novel, authentic looking data! Very exciting stuff.

The demo code for this video is a set of adversarial Gaussian Distribution Curves in Python using Theano and PyPlot:

https://github.com/llSourcell/Generative-Adversarial-Network-Demo

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

I introduce two papers in this video

Generative Adversarial Networks:

https://arxiv.org/pdf/1406.2661v1.pdf

and the associated code:

https://github.com/goodfeli/adversarial

Generative Adversarial Text-to-Image Synthesis:

https://arxiv.org/pdf/1605.05396v2.pdf

and it's associated code is here:

https://github.com/reedscot/icml2016

Another really cool repo using GANs:

https://github.com/Newmu/dcgan_code

Great explanation of GANs:

http://soumith.ch/eyescream/

Live demo of a GAN:

http://cs.stanford.edu/people/karpathy/gan/

One more really great description of generative models:

https://openai.com/blog/generative-models/

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment.
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26.3 Tone Analysis - Fresh Machine Learning #3

http://www.youtube.com/watch?v=89FHXM2q36s

This episode of Fresh Machine Learning is all Tone Analysis. Tone analysis consists of not just analyzing sentiment (positive or negative), but also analyzing emotions as well as writing style. There are a lot of dimensions to tone, and in this episode I talk about what I consider to be 3 seminal papers in this field. At the end of the episode, we use IBM’s Watson Tone Analyzer API to build our own tone analysis web app.

The demo code for this video can be found here:

https://github.com/llSourcell/Tone-Analyzer

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

I introduce three papers in this video

Convolutional neural networks for sentence classification:

http://emnlp2014.org/papers/pdf/EMNLP2014181.pdf

Text categorization using LSTM for region embeddings:

http://arxiv.org/pdf/1602.02373v2.pdf

Hierarchical attention networks for document classification:

https://www.cs.cmu.edu/~diyiy/docs/naacl16.pdf

More info about the IBM Watson Tone Analyzer API:

http://www.ibm.com/watson/developercloud/tone-analyzer.html

Some great notes, slides, and practice problems for NLP:

http://cs224d.stanford.edu/syllabus.html

Live demo of the Watson Tone Analyzer:

https://tone-analyzer-demo.mybluemix.net/

Really great long-form page talking about text classification

http://www.nltk.org/book/ch06.html

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment.
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26.4 Generate Rap Lyrics - Fresh Machine Learning #4

http://www.youtube.com/watch?v=yE0dcDNRZjw

This episode of Fresh Machine Learning is about generating rap lyrics! Lyrical generation is possible using either Hidden Markov Models or deep learning. In this episode, I go through a few past examples of what's been done before, then dive into our own example that we can code in Python. Welcome to the machine MC revolution!

The demo code for this video can be found here:

https://github.com/llSourcell/Rap_Lyric_Generator

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Try it out live here:

http://deepbeat.org/

I introduce three papers in this video

Unsupervised Rhyme Scheme Identification in Hip Hop Lyrics Using Hidden Markov Models:

http://link.springer.com/chapter/10.1007%2F978-3-642-39593-2_3

Modeling Hip Hop Challenge-Response Lyrics as Machine Translation:

http://www.illc.uva.nl/LaCo/CLS/papers/wu_hiphop_itg.pdf

DopeLearning: A Computational Approach to Rap Lyrics Generation:

http://arxiv.org/abs/1505.04771

More info about Hidden Markov Models:

https://www.youtube.com/watch?v=TPRoLreU9lA

https://www.quora.com/What-is-a-simple-explanation-of-the-Hidden-Markov-Model-algorithm

http://www.developerstation.org/2011/11/hidden-markov-models-for-dummies.html

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment.
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26.5 Build an Autoencoder in 5 Min - Fresh Machine Learning #5

http://www.youtube.com/watch?v=GWn7vD2Ud3M

This video is all about autoencoders! I start off explaining what an autoencoder is and how it works. Then I talk about some use cases for autoencoders and the special types of autoencoders we use for each of them. Finally, I programmatically go through an example of a simple autoencoder, followed by a demo.

The code for this video is here:

https://github.com/llSourcell/autoencoder_demo/tree/master

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Autoencoder live demo in the browser:

https://cs.stanford.edu/people/karpathy/convnetjs/demo/autoencoder.html

and here are some great links to read up on autoencoders:

http://ufldl.stanford.edu/tutorial/unsupervised/Autoencoders/

http://lazyprogrammer.me/a-tutorial-on-autoencoders/

https://blog.keras.io/building-autoencoders-in-keras.html

https://www.quora.com/What-are-the-best-resources-for-learning-about-autoencoders-from-scratch

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:

https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment.
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26.6 Build a Self Driving Car in 5 Min - Fresh Machine Learning #6

http://www.youtube.com/watch?v=hBedCdzCoWM

Let's build a self driving car! In this video, I talk about how self driving cars work, then dive into 2 fresh papers that add modern improvements to autonomous vehicles. The self driving car that we build is in a simulated environment and is built using PyGame and the Keras machine learning library.

The code in the video is here:

https://github.com/llSourcell/Self-Driving-Car-Demo/

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Paper 1: Long term Planning for Short Term Prediction

http://arxiv.org/pdf/1602.01580v1.pdf

Paper 2: End-to-End Learning for Self-Driving Cars

https://arxiv.org/pdf/1604.07316v1.pdf

More on Reinforcement Learning:

http://www2.hawaii.edu/~chenx/ics699rl/grid/rl.html

https://www.quora.com/Artificial-Intelligence-What-is-an-intuitive-explanation-of-how-deep-Q-networks-DQN-work

http://www2.econ.iastate.edu/tesfatsi/RLUsersGuide.ICAC2005.pdf

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:

https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment.
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26.7 Build an Antivirus in 5 Min - Fresh Machine Learning #7

http://www.youtube.com/watch?v=iLNHVwSu9EA

In this video, we talk about how machine learning is used to create antivirus programs! Specifically, a classifier can be trained to detect whether or not some piece of software is malicious.

Check out my friend Danooct1's Youtube channel on viruses (dope AF):

https://www.youtube.com/user/danooct1

The code in the video is here:

https://github.com/llSourcell/antivirus_demo

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Paper 1: A Machine Learning Approach to Anomaly based detection on Android

https://arxiv.org/pdf/1512.04122.pdf

Paper 2: SMARTBot - A Behavior Detection Framework for Botnets

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4792466/

Paper 3: A New Malware Detection Approach Using Bayesian Classification

https://arxiv.org/pdf/1608.00848v1.pdf

More on Machine Learning + Cybersecurity:

http://www.lancaster.ac.uk/pg/richarc2/dissertation.pdf

https://www.sec.in.tum.de/malware-detection-ws0910/

https://insights.sei.cmu.edu/sei_blog/2011/09/using-machine-learning-to-detect-malware-similarity.html

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:

https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment.
Follow me:
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27 Machine Learning for Hackers

27.1 Your First ML App - Machine Learning for Hackers #1

http://www.youtube.com/watch?v=2FOXR16mLow

This video will get you up and running with your first ML app in just 7 lines of Python. The app will be able to recognize Iris flowers.

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Follow the install instructions for TensorFlow here:
https://www.tensorflow.org/versions/r0.8/get_started/os_setup.html#pip-installation

Follow the install instructions for SciKit Learn here:
http://scikit-learn.org/stable/install.html

And here is a link to the repo for Skflow (the scikit interface for TensorFlow):
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/learn/python/learn

My code sample is in the README of that repo under "Linear Classifier".

Map of Machine Learning Models:
http://www.wangbo.info/img/mlmindmap.png

Map to pick the right model from SciKit Learn (although this doesn't take into account deep neural nets [just think -- lots of data? Just go with the DNN]):
http://1.bp.blogspot.com/-ME24ePzpzIM/UQLWTwurfXI/AAAAAAAAANw/W3EETIroA80/s1600/drop_shadows_background.png

This is the first in my new application-focused machine learning series. The goal is to avoid anything math-heavy and focus on building things with machine learning libraries.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
https://www.patreon.com/user?ty=h&u=3191693

Much more to come so please subscribe, like, and comment. That stuff is what encourages me to continue!
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27.2 Build an AI Composer - Machine Learning for Hackers #2

http://www.youtube.com/watch?v=S_f2qV2_U00

This video will get you up and running with your first AI composer in just 10 lines of Python. The app can compose british folk songs after training on an existing folk dataset.

The code for this video is here:
https://github.com/llSourcell/AI_Composer

I created a Slack channel for us, sign up here:
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This is 'a' way to generate music, it's not necessarily the absolute best way. Another attempt I really like is this one since it can generate not just monophonic music, but polyphonic music as well:

http://www.hexahedria.com/2015/08/03/composing-music-with-recurrent-neural-networks/

Tensorflow install instructions here:
https://www.tensorflow.org/versions/r0.8/get_started/os_setup.html#pip-installation

In a future video, I'll discuss how to easily use cloud GPU computing. Likely using www.fomoro.com

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
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27.3 Build a Game AI - Machine Learning for Hackers #3

http://www.youtube.com/watch?v=HBAUeJkFMH0

This video will get you up and running with your first game AI in just 10 lines of Python. The AI can theoretically learn to master any game you train it on, but has only been tested on 2D Atari games so far.

The code for this video is here:
https://github.com/llSourcell/Game-AI

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Tensorflow install instructions here:
https://www.tensorflow.org/versions/r0.8/get_started/os_setup.html#pip-installation

Gym install instructions here:
https://gym.openai.com/docs

Great course on the brain (I really love this course):
https://www.mcb80x.org/

Original Deep Q Learner Paper:
https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf

Lots of info on convolutional neural networks:
http://cs231n.github.io/convolutional-networks/

Lots of info on reinforcement learning:
http://www.nervanasys.com/demystifying-deep-reinforcement-learning/

I'm a fan of www.fomoro.com for cloud GPU computing since they are the only free-to-try cloud GPU provider I could find. Let me know if you find another!

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
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27.4 Build a Movie Recommender - Machine Learning for Hackers #4

http://www.youtube.com/watch?v=eKmIVU8EUbw

This video will get you up and running with your first movie recommender system in just 10 lines of C++. We train a neural network on a MovieLens dataset of movie ratings by different users to generate a top 10 recommendation list for the default user ID.

The code for this video is here (everything included):
https://github.com/llSourcell/Movie_Recommender

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

The Original Amazon DSSTNE code is here:
https://github.com/amznlabs/amazon-dsstne

Link to AWS:
https://aws.amazon.com/

Link to FileZilla:
https://sourceforge.net/projects/filezilla/

Paper I found pretty cool (a deep learning based rec system):
https://arxiv.org/pdf/1409.2944.pdf

And a correction -- Scott grand recently tested it vs Tensorflow and reported not just a 2x, but a 15x speedup:
https://medium.com/@scottlegrand/first-dsstne-benchmarks-tldr-almost-15x-faster-than-tensorflow-393dbeb80c0f#.loze1hltg

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

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27.5 Build an AI Artist - Machine Learning for Hackers #5

http://www.youtube.com/watch?v=9Mxw_ilpvwA

This video will get you up and running with your first AI Artist using the deep learning library Keras!

The code for this video is here:
https://github.com/llSourcell/AI_Artist

I created a Slack channel for us, sign up here:
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Here's the initial Google DeepDream blog post:
http://googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-into-neural.html

A Deepdream web app:
https://dreamscopeapp.com/

The Neural Style Paper:
http://arxiv.org/pdf/1508.06576v2.pdf

Some great info on convolutional neural networks:
http://colah.github.io/posts/2014-07-Conv-Nets-Modular/

You should train this baby in the cloud using AWS. See ML for Hackers #4 for a tutorial on how to use AWS:
https://www.youtube.com/watch?v=eKmIVU8EUbw

This person went ahead and made a web app so you don't even have to compile the code to try this out:
https://deepart.io/

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
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27.6 Build a Chatbot - ML for Hackers #6

http://www.youtube.com/watch?v=5_SAroSvC0E

This video will get you up and running with your first Chatbot using the deep learning library Torch!

The code for this video is here:
https://github.com/llSourcell/Chatbot-AI

I created a Slack channel for us, sign up here:
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Here's the Neural Conversational Model paper (check out the machine-generated support conversations, they're mind-blowingly good):
http://arxiv.org/pdf/1506.05869v3.pdf

You should train this baby in the cloud using AWS. See ML for Hackers #4 for a tutorial on how to use AWS:
https://www.youtube.com/watch?v=eKmIVU8EUbw

Some great info on LSTM architecture:
http://deeplearning4j.org/lstm.html

Link to Facebook's Chatbot API if you're curious:
https://developers.facebook.com/blog/post/2016/04/12/bots-for-messenger/

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
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27.7 Build an AI Reader - Machine Learning for Hackers #7

http://www.youtube.com/watch?v=AKwfVAKaigI

This video will get you up and running with your first AI Reader using Google's newly released pre-trained text parser, Parsey McParseface.

The code for this video is here:
https://github.com/llSourcell/AI_Reader

I created a Slack channel for us, sign up here:
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Here's the original blog post about Parsey:
https://research.googleblog.com/2016/05/announcing-syntaxnet-worlds-most.html

This is Google's repo for Parsey:
https://github.com/tensorflow/models/tree/master/syntaxnet

If you're interested in NLP, check out Michael Collins course. This guy is such a G (he co-authored Parsey), I took this class at Columbia and it was one of the few where I actually attended every session. (it's free and open source!):
https://www.coursera.org/course/nlangp

Link to API.AI in case you want to go that route:
https://api.ai/

The political debate fact checker was an idea I had but never got around to building. It takes the transcript from a political debate, extracts the intent of a claim, queries it against google, perhaps scrapes some search result data and then assigns it a truthfulness rating out of 100. If it falls below a certain threshold, that person must be lying! How cool would that be?

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
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27.8 Build an AI Writer - Machine Learning for Hackers #8

http://www.youtube.com/watch?v=x24VEUEph0Q

This video will get you up and running with your first AI Writer able to write a short story based on an image that you input.

The code for this video is here:
https://github.com/llSourcell/AI_Writer

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Great write-up on recurrent neural nets (LSTMs and GRUs)
http://deeplearning4j.org/lstm.html

Paper on skip thought vectors:
http://arxiv.org/pdf/1506.06726v1

Paper on Unifying Visual Semantic Embeddings:
https://arxiv.org/pdf/1411.2539v1.pdf

You can test this code out at this site! It's really cool, they have a bunch of deep learning models in the cloud, you just have to upload an input and it gives you an output:
http://www.somatic.io/models/2n6g7RZQ

If you're interested in NLP, check out Michael Collins course. This guy is such a G (it's free and open source!):
https://www.coursera.org/course/nlangp

And check out this guy's free deep learning course on Udacity:
https://www.udacity.com/course/deep-learning--ud730

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
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27.9 Build a Chatbot w/ an API - ML for Hackers #9

http://www.youtube.com/watch?v=c6R3EjMQ7H0

This video will get you up and running with your first API-based chatbot able to converse with a user around a topic of your choosing!

The code for this video is here:
https://github.com/llSourcell/API_Chatbot

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

API.AI (My favorite Chatbot API):
https://api.ai/

Nuance Mix:
https://developer.nuance.com/mix

Wit.Ai:
https://wit.ai/home

SiriKit:
https://developer.apple.com/sirikit/

Chatbots are all about recognizing intent i.e what is the user saying to me? I thought this paper was really fascinating. It's called "Toward Computational Recognition of Humorous Intent":
https://www.researchgate.net/profile/Julia_Taylor2/publication/228353850_Toward_computational_recognition_of_humorous_intent/links/0deec53b55d7fd4782000000.pdf

I love you guys! Thanks for watching my videos, I do it for you. I left my awesome job at Twilio and I'm doing this full time now.

I recently created a Patreon page. If you like my videos, feel free to help support my effort here!:
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28 Intro to Decentralized Apps

28.1 5 Reasons to Build Decentralized Apps

http://www.youtube.com/watch?v=utmnexPw1bY

Cool paper on Decentralized Apps: https://github.com/DavidJohnstonCEO/DecentralizedApplications
My book on Decentralized Apps:
http://www.amazon.com/Decentralized-Applications-Harnessing-Blockchain-Technology/dp/1491924543
IPFS: http://www.ipfs.io
Ethereum: https://www.ethereum.org/

I created a Slack channel for us, sign up here:
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28.2 The Interplanetary File System

http://www.youtube.com/watch?v=uypCBHLacBc

IPFS website: https://ipfs.io
Decentralized apps video: https://www.youtube.com/watch?v=utmnexPw1bY

I created a Slack channel for us, sign up here:
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28.3 How to Build a Dapp in 3 min

http://www.youtube.com/watch?v=OdN10MnU5gs

Link to Embark Dapp Framework:
https://github.com/iurimatias/embark-framework

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Link to Ethereum:
https://ethereum.org/

Link to IPFS:
https://ipfs.io/

Link to My Decentralized Apps Book:
http://www.amazon.com/Decentralized-Applications-Harnessing-Blockchain-Technology/dp/1491924543

Link to Decentralized Apps Paper:
https://github.com/DavidJohnstonCEO/DecentralizedApplications

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28.4 4 Ways to Use Smart Contracts

http://www.youtube.com/watch?v=hm4Eym4N0_Q

Ethereum smart contract tutorials: https://ethereum.gitbooks.io/frontier-guide/content/writing_contract.html

I created a Slack channel for us, sign up here:
https://wizards.herokuapp.com/

Smart contract wiki: https://en.bitcoin.it/wiki/Contract

Voting app description: https://medium.com/@DomSchiener/publicvotes-ethereum-based-voting-application-3b691488b926#.3qm3zrdz1
Voting app repo: https://github.com/domschiener/publicvotes

Smart property startup: http://airlock.me/

Decentralized Uber: http://www.lazooz.net/

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28.5 3 Dapps You HAVE to See

http://www.youtube.com/watch?v=vCBCyO7SE5I

La'Zooz: http://www.lazooz.net/
OpenBazaar: https://openbazaar.org/
Synereo: http://www.synereo.com

I created a Slack channel for us, sign up here:
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My Book on DApps: http://www.amazon.com/Decentralized-Applications-Harnessing-Blockchain-Technology/dp/1491924543

Awesome Paper on DApps: https://github.com/DavidJohnstonCEO/DecentralizedApplications

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28.6 Char's Life as a BitTorrent Engineer

http://www.youtube.com/watch?v=gmD_RnOspS4

Check out BitTorrent: http://www.bittorrent.com/

I created a Slack channel for us, sign up here:
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Would love any feedback/opinions/questions in the comments section. Please subscribe if you haven't yet, thanks guys!

EDIT: I now have a professional grade microphone guys. Every video after this one will have awesome audio quality.

-Siraj

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29 Intro to Bitcoin

29.1 What is Bitcoin?

http://www.youtube.com/watch?v=nVFDZsxOMRg

Comment! Like! Subscribe!

I created a Slack channel for us, sign up here:
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Buy your first Bitcoin here: http://www.coinbase.com
Bitcoin source code: https://github.com/bitcoin/bitcoin
Cheap Bitcoin Miner: https://21.co/learn/
Expensive Bitcoin Miner: http://www.butterflylabs.com/
Good tutorials on building your first BTC apps: https://21.co/learn
Great free online class for learning more about BTC: https://www.youtube.com/watch?v=fOMVZXLjKYo

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29.2 5 Ways to Use Bitcoin

http://www.youtube.com/watch?v=TkWHsmOthzw

Example of a USD pegged cryptocurrency: https://nubits.com/
Create your own cryptocurrency using Colored Coins: https://www.coinprism.com/
Stellar: https://www.stellar.org/
GridCoin: http://www.gridcoin.us/
ZeroCoin: http://zerocoin.org/
LiteCoin: https://litecoin.org/

I created a Slack channel for us, sign up here:
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29.3 BTC Fever - Siraj [Music Video]

http://www.youtube.com/watch?v=dDqye8F4R6s

Available on SoundCloud: http://bit.ly/1Jktv9C
Click here to share this on Twitter: http://bit.ly/1mpqlY8
Click here to post this on Facebook: http://on.fb.me/1R2OLDg

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=== Lyrics ===

INTRO

Shoutout to all them bitcoin warriors out there
Keep fighting the good fight. Don’t be afraid of the feds. Actually a healthy bit of caution is good but. Yeah. anyway

VERSE 1

Check my temperature, damn i got a fever
it’s spelled BTC yeah i’m a believer
the revolution started back in 2008
Satoshi put pen to paper said it aint too late

the future is now but money exchange is so behind
lets make currency and cryptography align
money and cryptography a beautiful match
send a mil in a minute straight to Kenya and back

no middle men, no limits, no chargebacks
make them digital stacks no wasting time on greenbacks
corruption is enemy, all power centrally
caging us we aint free oh so so many fearfully

network is owned by many not just the few
if you wanna join open a wallet we’ll include you
that’s right i’m the bitcoin baron
just follow me i’ll take you to bitcoin heaven

HOOK

Freedom of currency
Frees us from tyranny
I said I couldn’t be
without you BTC

Freedom of currency
Frees us from tyranny
I said I couldn’t be
without you BTC

VERSE 2

i rock blockchains playin all them stock games
it’s a public record with no central effort, uh
open source protocol is straight up facts
miners carry SHA 256 on they backs

they be generatin twenty one million coins
make it rain bitcoin everyday free to join
banking millions of peeps from china to brazil
everyone wants a piece of that digital shill

They say absolute power corrupts absolutely
well bitcoin disrupts that very same power rudely
i’m looking at you politicians who lie
you be running but aint nowhere to hide

It’s a network that proves its fair and just
We don’t need a God ‘in encryption we trust’!
I have bitcoin dreams of a world that is free
a fantasy of BTC creativity

HOOK

Freedom of currency
Frees us from tyranny
I said I couldn’t be
without you BTC

Freedom of currency
Frees us from tyranny
I said I couldn’t be
without you BTC

VERSE 3

if you a bitcoin warrior stand up
we the chosen ones who never give up
we’ll grow this beast from the west to the east
feds’ll try to stop us we can’t be policed

it started with one and now its millions
but just wait pretty soon its billions
yea. thats right.worldwide recognize.
you can’t stop us. decentralized.

uh. public ledger.
No secrets. Ever.
heh. Think about it.

===Shoutout===

Corey Brier the awesome cameraman!

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