Found 6 comments on HN
danieldk · 2019-03-02 · Original thread
The list does not describe why they are the best books, except for a very short blurb. We read the Deep Learning book by Goodfellow, Bengio, and Courville in our reading group when it came out. Even though it contains useful information, it is written in a very haphazard fashion. It is also very unclear what its target audience is. Some sections start as a foundational description, to suddenly change into something that is only for readers with a strong maths background. No one in the reading group was enthusiastic about the book and most actively recommend against it (some called it 'the deep learning book for people who already know deep learning').

The highest-rated Amazon reviews seem to have come to the same conclusion: https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...

Put differently, a list such as the linked one may attract a lot of visitors. But without critical, in-depth reviews it is not very useful and might set potential learners on the wrong path.

petilon · 2019-02-19 · Original thread
I ordered a copy of Deep Learning [1] from Amazon last week. On Barnes & Noble [2] the book costs $76.80. On Amazon it is just $28.00. I received the book a couple of days ago. The pages look like it was printed using a low-resolution printer, and the ink color is uneven across pages. I am returning the book. Possible counterfeit, sold by a third party seller. On the other hand this book is also available free online [3]. Maybe it is legal to print it and sell it?

[1] https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...

[2] https://www.barnesandnoble.com/w/deep-learning-ian-goodfello...

[3] https://www.deeplearningbook.org/

henning · 2018-12-25 · Original thread
Would you enjoy something that gives a broad overview? Norvig's AI book https://www.amazon.com/Artificial-Intelligence-Modern-Approa... 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 http://aima.cs.berkeley.edu/ 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 - https://www.amazon.com/Deep-Learning-Python-Francois-Chollet... - which might be more directly relevant to your interests.

There's also what I guess you would call "the deep learning book". https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...

(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 (http://cs231n.stanford.edu/) and the Goodfellow/Bengio/Courville Deep Learning book (https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...)

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

http://www.deeplearningbook.org/

Seems to have good reviews on Amazon:

https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...

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)

https://www.amazon.com/Deep-Learning-Adaptive-Computation-Ma...

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