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Deep Learning

(Adaptive Computation and Machine Learning)

4.44  ·  Rating details ·  1,236 ratings  ·  93 reviews
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge fro
Hardcover, 778 pages
Published December 9th 2016 by The MIT Press (first published November 2016)
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The_phet There are some "introductory" chapters, but they are really more about refreshing stuff than learning from scratch.

They say the book can be read by pe…more
There are some "introductory" chapters, but they are really more about refreshing stuff than learning from scratch.

They say the book can be read by people starting, but that is false. You need really really strong math knowledge, and you need very advanced machine learning knowledge.

If you want a real introduction to deep learning, I suggest you try another book.(less)

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Average rating 4.44  · 
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 ·  1,236 ratings  ·  93 reviews

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Jul 19, 2017 rated it it was amazing  ·  review of another edition
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, ...more
Van Huy
Oct 26, 2016 rated it it was amazing  ·  review of another edition
Part 1: basic math and machine learning, no problem.
Part 2: the part I like the most. It includes almost everything we need to know to adapt deep learning algorithms to practical matters.
Part 3: still feeling meh. It's too difficult for me to understand at this moment. Maybe I will come back after finishing PRML book.
Jan 19, 2018 rated it did not like it  ·  review of another edition
I decided to read this book because I wanted to learn about Deep Learning, and everywhere I looked on the Internet seemed to point in this direction as the book you need to read to learn about DL. I gave up around page 220 (this is at the end of chapter 6), when I realized that I was not learning anything, but not only that, I was getting confused about topics I already knew.

Before I go on detail, just a bit about my background. I am a Comp Sci researcher in my early 30s, working in a university
Elie De Brauwer
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 w ...more
Feb 12, 2018 rated it it was amazing
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. It teaches how to approximate approximations of approximations due to life's intractability.
Hamish Seamus
I wasn't able to follow beyond about half way.

* Following the success of back-propagation, neural network research gained popularity and reached a peak in the early 1990s. Afterwards, other machine learning
techniques became more popular until the modern deep learning renaissance that began in 2006.
* Regularization of an estimator works by trading increased bias for reduced variance.
* in neural networks, typically only the weight and not the biases are used in normalisation penalties
* Effect of
Luke Duncan
This is a dense and challenging read, but currently “the Bible” of machine learning. Walk through any Machine Learning teams offices in Silicon Valley and you’ll find this book leaning against a monitor somewhere. I invested a lot of time early on with the book, getting mentors for different sections. Some of it was over my head, others deeper than I needed to go. I appreciated the breadth it gave of the topic but this definitely isn’t a book for someone new to the field of ML. It presumes a lot ...more
Tomasz Bartczak
Mar 16, 2017 rated it really liked it  ·  review of another edition
Shelves: software
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. For me it was a great way to organize all the bits I had about deep learning. Part III was too hard for a practitioner like me so I just skimmed through.
Nov 15, 2016 rated it it was amazing  ·  review of another edition
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 - some important concepts are described in too high level detail, so a complementary material is needed to fully understand the algorithms in detail.
Aiham Taleb
Jun 01, 2018 rated it really liked it  ·  review of another edition
It’s very difficult to review this book in the means of goodreads. It provides tremendous amount of detail for neural networks and especially the deep versions of them. The writers succeeded in finding an appropriate way to categorize the topics in a way that conveyed the ideas smoothly.
Victoria Krakovna
An excellent, comprehensive textbook on deep learning.
Arman Behrad
Aug 01, 2020 rated it really liked it  ·  review of another edition
Very comprehensive, from Linear Algebra to Probability theory and Deep learning algorithm. Such a Bible in it‘s area.
read parts... this is a fat encyclopedia, not a how-to manual
Shubhendu Trivedi
Mar 13, 2016 rated it really liked it  ·  review of another edition
I volunteered to present some of the material central to Modern Neural Networks in a bunch of class presentations, lectures to undergrads in my undergrad institution and reading groups from December 2015 to March 2016, and used that as an excuse to read this book page by page, and used it to make my presentation slides. I am glad it exists, as it summarizes much of the history and the recent work in Neural Networks. The earlier book on the subject (A Foundations and Trends volume by Bengio - Lea ...more
Terran M
Mar 27, 2018 rated it it was amazing
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 ...more
Jul 20, 2018 rated it really liked it  ·  review of another edition
I rated this book a bit higher than I might have otherwise as it is operating at the edge of what research was at the time it was written. It's a pretty strong rundown in that regard.

The negative side is that it obfuscates its information by its presentation. It's not motivated well -- if I wasn't already familiar with most of it, it might have been harder to grasp, but I can't test that hypothesis. Some people complain about the math in the reviews -- I don't as math can be self-explanatory. Bu
Hampus Wessman
Jul 17, 2018 rated it it was amazing
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. Look elsewhere both for practical instructions on how to use various tools and frameworks (e.g. Tensorflow) and also for a
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. I definitely prefer reading articles (including brilliant articles by the Authors of this book).
Mar 08, 2018 rated it really liked it  ·  review of another edition
This tries to be the clr of deep learning. But it might be too early for that so the last part is more experimental. Also, statistics is different than real math so all the proofs don't make much sense.
Oleg Dats
May 20, 2019 rated it it was amazing  ·  review of another edition
If you ask me about only one book about Deep Learning I would suggest this one. It covers everything. Starting with fundamentals like linear algebra, probability, statistics, optimizations and finishing with deep neural nets.
Just an amazing book for studying the field.
Apr 23, 2016 rated it really liked it  ·  review of another edition
The best advanced introduction textbook I ever read. Accessible and clear without being too watered down. Even gave me some refreshing insights about things I'd thought I fully understood.
H. Trieu Trinh
Apr 23, 2017 rated it really liked it  ·  review of another edition
Great introduction, covers many popular modern neural architectures as of 2017. Part 3 is somewhat not very helpful to practitioners like me.
Dennis Cahillane
Jan 25, 2020 rated it really liked it  ·  review of another edition
This book is for grad students, advanced practitioners and theoreticians, so I (a hobbyist engineer) was only able to read about the first half. It works well to gauge your level of understanding of how deep learning is implemented.

My big takeaway from the book is that for my purposes I don’t need to understand these implementation details, even if I find them very interesting, because anything worth doing is implemented in libraries and by cloud providers. A hobbyist engineer like me can run co
Oct 12, 2020 rated it it was amazing  ·  review of another edition
Finally made my way through the bulk of this, has all the fundamentals of the core DL concepts in really good depth along with some of the more specific and recent innovations. One to continually refer back to.
Lee Richardson
May 24, 2019 rated it it was amazing  ·  review of another edition
Shelves: math-statistics, ai
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. ...more
Jared Tobin
Jun 09, 2017 rated it it was amazing  ·  review of another edition
This is an astoundingly good book. I admit I had sort of attributed its high rating to the general popularity of deep learning as of late, but the book really is a remarkable achievement. It is a close-to-exhaustive summary of the state of the art in deep learning and related techniques; the text is clean and the presentation is elegant and rigorous throughout, without what I could perceive as the slightest misstep.

What really astounds me about this book is not its quality per se - it's that it
Filippo Pacifici
Deep Learning is the most detailed and comprehensive book I read about AI (specifically neural networks) so far.
It is not an easy book. If you do not have a sound mathematical background it will be very hard. The author does a great job in one of the first chapter in providing such background in a sound way.
This is not to be considered a simple tutorial to build your machine learning algorithm. This book can be a resource both for practitioner and for researchers since it goes deep into the theo
May 24, 2017 rated it it was amazing  ·  review of another edition
Shelves: computer-science
Currently the bible for deep learning. I am a grad student at the TU Vienna and I just read the book cover to cover for my master thesis. I have already designed and optimized neural networks before reading this book and done my fair share of tutorials and practices beforehand, but I still managed to find lots of things I did not think about before, like using a dataset with increasing difficulty.

I can personally recommend this book for anyone who wants to use deep learning in his company or fo
Jan Van de Poel
I read about as much as I can find about deep learning and this book come recommended top of the list. After a first attempt, I decided to read some more practical introductions first, wanting to get my hands dirty and read up on all the nitty-gritty once I had more experience.

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 i
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Ian J. Goodfellow is a researcher working in machine learning, currently employed at Apple Inc. as its director of machine learning in the Special Projects Group. He was previously employed as a research scientist at Google Brain.

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