Found 4 comments on HN
henning · 2018-12-25 · Original thread
Would you enjoy something that gives a broad overview? Norvig's AI book should give you a very broad perspective of the entire field. There will be many course websites with lecture material and lectures to go along with it that you may find useful.

The book website has lots of resources.

But it sounds like you are specifically interested in deep learning. A Google researcher wrote a book on deep learning in Python aimed at a general audience - - which might be more directly relevant to your interests.

There's also what I guess you would call "the deep learning book".

(People have different preferences for how they like to learn and as you can see I like learning from books.)

(I apologize if you already knew about these things.)

mbeissinger · 2018-05-03 · Original thread
Our vision is to be the tool that starts with great settings for beginners but lets you graduate into the internals as you become more expert - at the lowest level you can interactively create computation graphs and see their results as you change settings, sort of like eager mode for ml frameworks on steroids (or other visual computation graph programs that designers use like Origami/Quartz Composer).

The lobes in the UI are all essentially functions that you double click into to see the graph they use, all the way down to the theory/math.

If you want more comprehensive ways to learn the theory, I highly recommend Stanford's 231n course ( and the Goodfellow/Bengio/Courville Deep Learning book (

melling · 2017-09-24 · Original thread
Here’s the book that’s mentioned:

Seems to have good reviews on Amazon:

jrbedard · 2017-01-16 · Original thread
Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow, Yoshua Bengio, Aaron Courville

Came out in November 2016. Split in 3 parts:

Part I: Applied Math and Machine Learning Basics (Linear Algebra, Probability and Information Theory, Numerical computation)

Part II: Deep Networks: Modern Practices (Deep Feedforward Networks, Regularization, CNNs, RNNs, Practical Methodology & Applications)

Part III: Deep Learning Research (Linear Factor Models, Autoencoders, Representation Learning, Structured Probabilistic Models, Monte Carlo Methods, Inference, Partition Function, Deep Generative Models)

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