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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.




> it is written in a very haphazard fashion

I felt the same way. Knowledgeable authors, loads of information, but quite poorly written.

That said, I don’t know of another book that’s as up to date or comprehensive, so I guess we’re stuck with it till something better comes along.


It's a great book if you need references on the basic deep learning stuff for publications or your thesis. However, for getting started it is horrible.


What books did your group find well written? It would be very helpful for outsiders to know what people who know their stuff consider good learning material.


I find it ironic that none of the DL promoters ever apply DL in their own promotions.

This book list would have been much better if promoters would have taken time to apply DL to reviews of promoted books and share results.

Whole DS/AI/DL/ML area is infested with such lack of application on their own stuff.




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