Deep Learning by MIT Press

A comprehensive overview of the Deep Learning paradigm, written by several leading researchers in the field. The author's cover many topics, and did a great job providing references to the current literature in the field. For this reason, I see this book more as a reference book than a book to read straight through. I read it straight through, but there were definitely some sections I skimmed over, especially when the author's introduced technical details of several related methods in the field.

If you ask me about only one book about Deep Learning I would suggest this one. It covers everything.

The book describes the topic in an abstract and top-level mathematical point of view, that is succinct and generic, and yes, very much like reading papers. Thus it’s very intuitive mathematically, and not very intuitive the human way. And so, if reading paper is not one’s preference, I’d suggest avoid reading this book.

If you are beginner I would definitely not recommend this book. Though well structured and an exceptional book, it is very intense and dry.

The first chapters of the book are a great intro into the fundamentals, but as I progressed through the book, it felt like a list of topics, with a hint of math (I had hoped to get the raw details) and very few practical insights.

Not sure I would recommend this as a deep learning reference...

Reading this book was tiresome. Imagine extracting the most technical pieces of hundreds of publications and piling them all together into a single book. This really is a prescription for unreadable manual, and that's unfortunately what has happened to "Deep Learning" book.

A very good high-level overview of the most popular deep learning techniques at the time. I will keep it around as a reference for sure.

It requires some prior maths, statistics and machine learning knowledge, but is not a mathematical book with proofs and detailed abstract theory. The focus is on practically applicable theory at a high level, which it provides in a good way.

Really well structured. Includes a knowledge tree to explain where you can use the book for reference, and where certain chapter are dependent on others. Only criticism is the lack of depth of explanation for linear Algebra and probability theory, but for a book that is not about these subjects, definitely excusable.

I found this book to be an excellent introduction and overview of deep neural networks for someone who already understands other types of statistical and machine learning models. It can be a challenging book, but it's clear and well written; the challenge is commensurate with the inherent complexity of the material, and not because the authors capriciously skip steps. In fact, rather the opposite is the case - the authors are quite explicit and put in more intermediate steps in their derivations than most books, let alone papers, which I quite like.

This book is great for readers to gain intuition behind many of the concepts underpinning deep learning techniques taken for granted, with a focus on probabilistic graphical models towards the end.

If you are like me, someone who knows about ML or AI, and want to know about DL, or even if you are someone who doesn't know much about ML or AI, please run away from this now, don't do the mistake I did of devoting your time to a pointless process. I am not going to recommend any other book, because I don't want people to think I am here to shill, but there are way better options out there.

If you want to learn DL: run away. In my opinion this book is for people who are already expets in DL and want to go deep into its theoretical background. If that is your case, then probably this is perfect for you. It is probably also very good for PhD students or researchers who are researching about DL. If you want to use DL as a final end-user: run away.

I both appreciated this book and was frustrated by it. If you want an academic survey paper with cited sources in the form of a book this is the book for you. If you want an approachable introduction this probably isn’t it. The authors claim to be targeting two audiences: students and software engineers in industry. I think they understand the first audience much more than the second.