Deep Learning by MIT Press

This is apparently THE book to read on deep learning. Written by luminaries in the field - if you've read any papers on deep learning, you'll have encountered Goodfellow and Bengio before - and cutting through much of the BS surrounding the topic: like 'big data' before it, 'deep learning' is not something new and is not deserving of a special name. Networks with more hidden layers to detect higher-order features, networks of different types chained together in order to play to their strengths, graphs of networks to represent a probabilistic model.

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.

The "i'm finished" should more or less be interpreted as "i've had it". This book is both awesome and horrible. It's awesome because it is giving an extremely up to date view on what is currently state of the art. At this moment the book isn't even't published and it will be a landmark once it hits the shelves. It is horrible because it diguises all insights in maths, and partical use/application should be sought after (instead of being plain obvious). The latter means that this book is really written as an academic text (and I'm not part of the target audience ;-) ).

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.

A broad overview of the current state of deep learning. Given the introduction to machine learning in general it can be the position for learning "machine learning". Yet this is not a step-by-step tutorial, rather a place where one can start the reading and be redirected elsewhere for details.

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 isnt 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.

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

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.

THE most rigorous and up to date reference of deep learning algorithms that is almost self-contained. Though If you intend to learn deep learning from scratch this book will not suffice

The best advanced introduction textbook I ever read. Accessible and clear without being too watered down.

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.