Part 2 of fast.ai's free deep learning course for coders, building on Part 1 with more advanced practical deep learning techniques. It is taught through video lectures and notebooks with a strong focus on how to build models rather than theory.

Commenters point to it as a practical, hands-on route into deep learning that spends very little time on theory and lets learners follow videos at their own pace.

What commenters said (1)

1. Recommendscs7022018in “The Matrix Calculus You Need for Deep Learning”

   If you have at least some coding experience and you are interested in the practical aspects of ML/DL (i.e., you want to learn the how-to, not the why or the whence), my recommendation is to start with the fast.ai courses by Jeremy Howard (co-author of this "Matrix Calculus" cheat sheet) and Rachel Thomas[a]:

   * fast.ai ML course: http://forums.fast.ai/t/another-treat-early-access-to-intro-...

   * fast.ai DL course: part 1: http://course.fast.ai/ part 2: http://course.fast.ai/part2.html

   The fast.ai courses spend very little time on theory, and you can follow the videos at your own pace.

   Books:

   * The best books on ML (excluding DL), in my view, are "An Introduction to Statistical Learning" by James, Witten, Hastie and Tibshirani, and "The Elements of Statistical Learning" by Hastie, Tibshirani and Friedman. The Elements arguably belongs on every ML practitioner's bookshelf -- it's a fantastic reference manual.[b]

   * The only book on DL that I'm aware of is "Deep Learning," by Goodfellow, Bengio and Courville. It's a good book, but I suggest holding off on reading it until you've had a chance to experiment with a range of deep learning models. Otherwise, you will get very little useful out of it.[c]

   Good luck!

   [a] Scroll down on this page for their bios: http://course.fast.ai/about.html

   [b] Introduction to Statistical Learning: http://www-bcf.usc.edu/~gareth/ISL/ The Elements of Statistical Learning: https://web.stanford.edu/~hastie/ElemStatLearn/

   [c] http://www.deeplearningbook.org/

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