Ian GoodfellowPenguin Random House Deep Learning (Adaptive Computation and Machine Learning series)

This book serves as an excellent reference book but also as a book to settle down and contextualise your knowledge. For example, I went to read up on contrastive divergence which is often bunched together with restricted boltzmann machines (naturally). The text on contrastive divergence was within a practically self-contained chapter on monte carlo sampling. It was beautiful. The authors also succeed in contextualising these topics against all the necessary central theory but also the state of the art. This book deserves a place in anyone's collection even if you feel you possess other works which may contain the same topics.

This is a great theoretical book on deep learning, covering many topics. The writing is clear, the maths not too difficult if you concentrate, and is fairly self contained. There are lectures about the chapters of the book on you tube. Really useful if you combine it with another source, such as Rajesh Sharma's 2020 SIGGRAPH ML and Neural Networks course, available on you tube, which covers much of the same deep learning material but with python/tensorflow/keras code.

This is genuinely good reading. As any thorough monograph it hold the essential foundations needed to get to understand all the fun stuff. And the fun stuff there is then plenty of to dive into. Find this a must read to get going w deep learning if you want to get serious about it.

This is a good comprehensive textbook starting at the basics (math, statistics and fundaments) of Machine Learning and Deep Learning. It is well aligned with eg MOOC courses in Machine Learning should you want to deepen your understanding. However, there are of course newer books, but this is worth buying a reference and as said a comprehensive textbook.

It is a great book for those that want some theoritical understanding of methods that underpin deep learning technologies. It is not for those who just want to learn how to apply deep learning technologies without needing the maths and theories. It requires some understanding of linear algebra, advanced probability theories, vector calculus and optimisation to make the understanding of the book natural. The authors did well to present a refresher on these topics but I don't still think anyone who has got no primer courses on these topics before will be able to cope very well.

I am really enjoying the book. Light enough for a good bedtime read, but with enough theory to be satisfying. The underlying concepts are clearly expressed with good illustrative examples. So far all the maths has been second year engineering degree level so should be well within most technical peoples comfort zone.

Great book. It's one of the main DL reference works, comprehensive, and expertly written.RE: other reviewers that experienced printing issues. My copy is in perfect condition. No missing pages, no repeats, no corrupted images. Looks great.

The coloured diagrams in the book are blurred. Very poor print quality. The book feels cheap. Otherwise no major problems.I happened to stumble upon the same book in a bookstore - and can confirm the book is very poorly printed.