Reinforcement Learning: An Introduction

Reinforcement Learning 2nd Ed By Sutton & Barto was surprisingly readable.

Get your feet wet: * http://cs231n.stanford.edu/ (the course notes are excellent) * http://neuralnetworksanddeeplearning.com/ * http://www.deeplearningbook.org/ Use Tensorflow to train a few small neural nets. Move on to CNNs and RNNs. Make sure you actually do this. By this point you'll have read a lot, and retain none of it if you don't put it to use. Look at reinforcement learning. Use the book by Sutton and Barto, the new edition: https://webdocs.cs.ualberta.ca/~sutton/book/the-book-2nd.htm... (https://webdocs.cs.ualberta.ca/~sutton/book/the-book-2nd.html) Read the first 4-5 chapters, then go online and read about Deep Q learning, policy gradients, DDPG, etc. Then try to solve some problems on OpenAI Gym. Once you have an idea of the kinds of problems you can solve, and have a couple you're interested in, go back and learn the foundational math, and start reading research papers. In general, start with modern books that mention deep learning. With older books or high-level-overview books, you'll get frustrated when you see something cool on /r/machinelearning and can't find any mention of it in the book.

Sutton & Barto's Reinforment Learning complete this triumvirate https://webdocs.cs.ualberta.ca/~sutton/book/the-book-2nd.htm... (https://webdocs.cs.ualberta.ca/~sutton/book/the-book-2nd.html) David Silver's Reinforcement Course is based on Sutton & Barto https://www.youtube.com/watch?v=2pWv7GOvuf0

Strang's class is very pretty and excellent and just a little bit off from the center of the sorts of linear algebra used in machine learning. Not a lot off, but a little off. A field that does inspire a lot of deep learning folks and never gets mentiond in this sort of thing is the theory of physical dynamical systems. Attractor is a term that came from here, for example, and much of the mathematics behind the numerical fuckery behind deep nets is dynamical in nature. RNN's are entirely dynamical systems. Classic there is Strogatz book (https://www.amazon.com/Nonlinear-Dynamics-Chaos-Applications... (https://www.amazon.com/Nonlinear-Dynamics-Chaos-Applications-Nonlinearity/dp/0738204536)). There is also information theory, of course, which is part of the MacKay source.…

I tend to get most of my CS content from papers & web pages, but Sutton & Barto's Reinforcement Learning book is pretty good: https://webdocs.cs.ualberta.ca/~sutton/book/the-book-2nd.htm... (https://webdocs.cs.ualberta.ca/~sutton/book/the-book-2nd.html)

I wish they'd make a new edition; but, probably, the field is moving too fast.

Sutton and Barto's book on reinforcement learning is available online at http://www.cs.ualberta.ca/~sutton/book/the-book.html, and is probably the definitive work on the subject.