Machine Learning Guide
This series aims to teach you the high level fundamentals of machine learning from A to Z. I'll teach you the basic intuition, algorithms, and math. We'll discuss languages and frameworks, deep learning, and more. Audio may be an inferior medium to task; but with all our exercise, commute, and chores hours of the day, not having an audio supplementary education would be a missed opportunity. And where your other resources will provide you the machine learning trees, I’ll provide the forest. Additionally, consider me your syllabus. At the end of every episode I’ll provide the best-of-the-best resources curated from around the web for you to learn each episode’s details.

  
Publisher
OCDevel
Website
http://ocdevel.com/podcasts/machine-learning
Genre
Software How-To   Podcasts   Technology  
collectionExplicitness
cleaned
trackExplicitness
cleaned

Latest Episode
You can check more episode on Publisher's website

Title
29. Reinforcement Learning Intro
Description

Introduction to reinforcement learning concepts

## Resources
- Hands-On Machine Learning with Scikit-Learn and TensorFlow (http://amzn.to/2tVdIXN) `book:medium` (last chapter)
- Sutton & Barto 2nd Ed PDF (http://incompleteideas.net/book/the-book-2nd.html) `book:hard`
- AI a Modern Approach. Website (http://aima.cs.berkeley.edu/), Book (http://amzn.to/2E02dEr) `book:hard`
- Berkeley cs294: Deep Reinforcement Learning (http://rll.berkeley.edu/deeprlcourse/) `course:hard`
- RL Course by David Silver (https://www.youtube.com/playlist?list=PLzuuYNsE1EZAXYR4FJ75jcJseBmo4KQ9-) `course|audio:hard`
- Convert video to audio:
** mp4 => mp3: `for f in *.mp4; do ffmpeg -i "$f" "${f%.mp4}.mp3" && rm "$f"; done`
** youtube => mp3: setup youtube-dl (https://github.com/rg3/youtube-dl) and run `youtube-dl -x youtube.com/playlist?list=`

## Episode
- RL definition: goal, rewards, actions
** Games (Atari, Chess, Go - Lee Sedol & Alpha Go)
** AI: learning, vision / speech, action / motion, planning
** Reasoning / knowledge vs model-based Deep RL?
** Reasoning / knowledge rep (+memory?) => Differential computers (https://deepmind.com/blog/differentiable-neural-computers/)
** vs supervised. Vision = supervised. Games = action. Trading can go both ways!
** Time: Credit assignment, delayed rewards, investment
- Model-based v free
** Policy (what you do; gut reaction)
- Value-based (Q-learning) vs Policy Gradient
** PG is direct: ML -> action
** Value-based indirect: Bellman stuff -> state/action values (Q-values) -> policy
- Openai Gym, cartpole
- Frameworks
** openai/baselines (https://github.com/openai/baselines)
** reinforceio/tensorforce (https://github.com/reinforceio/tensorforce)
** NervanaSystems/coach (https://github.com/NervanaSystems/coach)
** rll/rllab (https://github.com/rll/rllab)

Published
2018-02-05 16:50:51 UTC
http://ocdevel.com/podcasts/machine-learning


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