Starcraft AI blog

The recent textbook Deep Learning looks excellent to me. The authors are Ian Goodfellow, Yoshua Bengio, and Aaron Courville. There is an expensive edition, but it is also readable for free at the website. It’s a much-recommended book, and I recommend it too.

It is a theory book more than a practical book. I would say it is for people who have a computer background and perhaps don’t have deep math experience yet but aren’t afraid of math and are willing to dig in. I think it should be a good book for an early grad student, or an undergrad with strong interest, or a bright high schooler. The first part of the book presents the math knowledge you’ll need, like linear algebra and probability theory, so it is possible to start if you don’t know much. As always, the more background you have, the easier it gets.

To become expert, you have to know the theory and have experience applying it. If you want practical exercises to work your way into the technology, I think your approach should be to pick a software framework first (for example TensorFlow, Torch, Caffe) and then seek out tutorials or sample projects specific to the framework.

Everyone has their own learning style. If I were getting into deep learning from scratch, my approach would be: Read the whole book once through quickly to get an idea of the shape of things. With an overview in my mind, I could pick out parts that I needed to step through slowly and carefully. It takes time and practice to make unfamiliar concepts familiar, so if I hit topics where I felt weak or awkward I might seek out other resources.