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... 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.
* 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... 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.