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This book is possibly currently unique in its coverage of the latest ideas in the field of deep learning -- and it is a very convenient and good survey of fundamental concepts (linear algebra, optimization, performance metrics, activation function types), different network types (multi-layer perceptron, convolutional neural networks, and recurrent neural networks), practical considerations (data set, training and validation, implementation), and applications (comments on existing real-world/commercial uses). The final 235 pages of the content portion of the book is dedicated to topics in "Deep Learning Research", and these topics are truly at the current frontier.
Another reviewer said that one could gain the same knowledge of cutting-edge research by reading all of the latest papers (from academia and industry), but the "research" section of this book offers the following: Selection of the most notable research by the very experienced authors of the book, and collection of similar research in to a broader discussion of themes, and the additional insights. The book covers very advanced and new ideas currently being explored, and it is very nice to be able to have a consistent and coherent presentation of all of those ideas.
However, the book is also packed with valuable observations and pointers about more basic aspects of deep learning implementations and practices -- and such commentary is in depth and includes substantial analysis and mathematical derivation (in an intuitive presentation that often includes graphs illustrating the phenomenon).
As someone with an intermediate level of knowledge and experience of neural networks, I am really grateful for this book, because seems like the ideal resource for learning cutting-edge ideas and practices, with context. The book has excellent scope and depth, and I am confident that anyone with a solid background in linear algebra, calculus, statistics, and general machine learning, and basic neural networks (multi-layer perceptrons) will find this book to be very exciting and perhaps unique in its ability to take the reader to the next level and a new frontier. I was personally excited to learn about the idea of representing the dependencies of intermediate quantities by directed graphs, and how this can be used to perform calculations for recurrent neural networks efficiently. And I think the long chapter on recurrent neural networks is very helpful.
Having said all of this, I think only people with significant working knowledge and experience with neural networks and mathematics -- people whose academic or professional focus has been neural networks for at least a year or two -- would benefit from this book. This book answers a lot of the deeper questions that one is likely to have while developing a solid understanding of the fundamentals, and that's one of the book's tremendous values, but this book assumes an understanding of the fundamentals (but does briskly cover the basics).
I think this book is a perfect follow-up book for the excellent book "Neural Network Design (2nd edition)" by Hagan, Demuth, Beale, and de Jesus, and I highly recommend the latter for gaining the solid background needed to have a thrilling experience with the "Deep Learning" book.
In summary, I am very glad this "Deep Learning" book was written, and I think the "Deep Learning" book will be a great benefit to a lot of people, and to the evolution of the field.
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Customers say
Customers find the book provides a thorough introduction to deep learning concepts and offers a comprehensive overview of statistical methods. Moreover, the writing quality is well-received, with one customer noting it's written by the fathers of the field. Additionally, the book serves as a great reference with many references to quality papers and books throughout. However, customers disagree on its readability, with some finding it very easy to read while others consider it hard to understand. While the book covers both theory and practical considerations, some customers find it not very practical.
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125 customers mention comprehensive, 107 positive, 18 negative
...This is a very comprehensive, well-written, and easy-to-understand textbook on the theoretical foundations, current research, and applications of...Read more
An excellent book! A comprehensive yet efficient approach to Deep Learning.Read more
Thorough, best in class book for deeply understanding DL, and written by some of the top minds working in the field today....Read more
Excellent overview. This book is of appropriate depth and breadth and is the first of its kind, i.e., an academic treatment of deep learning....Read more
51 customers mention content, 39 positive, 12 negative
Content of the book is fantastic. The quality of this print is absolutely horrendous....Read more
Content is great. But......Read more
Deep Learning is an advanced book with great explanations and details....Read more
...The presentation style is unnecessarily terse, and dry, and is stylistically more similar to a research paper rather than a book. It is...Read more
26 customers mention writing quality, 21 positive, 5 negative
Simply marvellous. A great asset to any library. Very well written, complete and well thought out. My compliments and gratitude for a great book.Read more
...This is a very comprehensive, well-written, and easy-to-understand textbook on the theoretical foundations, current research, and applications of...Read more
...As I heard, the content of book is great. well written, lots of deep learning concepts and methods. However, the quality of book is not so good....Read more
Very good text with clearly explained concepts which serve well as a reference but it needs to be complemented with other resources for independent...Read more
15 customers mention reference, 15 positive, 0 negative
...While I am by no means an expert in the field, this has been a great reference and helped me bridge gaps in my knowledge....Read more
very good book, with many references to old and very recent works. a must-have if you're interested in the fieldRead more
Good reference book and resource for deep learning engineers.Read more
...And lastly, there is good history in here from people who know the space intimately....Read more
10 customers mention complexity, 7 positive, 3 negative
...There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to...Read more
I really like the content of this book and appreciate the mathematical rigor....Read more
concise. gets complicated math-wise fast - being realistic here, this isn't an easy subject. This is the best book on the subject.Read more
Very clear exposition, does the math without getting lost in the details....Read more
10 customers mention theory, 7 positive, 3 negative
Good book, with proper language. Some theory, some applications. Fairly comprehensive....Read more
...it's self-contained with even a quick review of linear algebra, probability and optimization. Some exercises would have been great....Read more
Strongly theoretical, i'd like to have code examples of the pseudocode presented in the book, however it's what i expected, the clearest...Read more
Pure theory. Very comprehensive. The introduction alone is an incredible recap of the history of machine learning/artificial intelligence....Read more
40 customers mention readability, 18 positive, 22 negative
...This material/book came in handy. Although it is wel written, it is a dry read.Read more
This book is a model of technical writing -- clear, precise, well-organized, and relentlessly helpful....Read more
...numerical demonstration of the derivation, it all seem vague and hard to understand....Read more
This is a very easy book to read. It explains concepts and themes in a very clear and understandable way....Read more
16 customers mention practicality, 11 positive, 5 negative
...well written, lots of deep learning concepts and methods. However, the quality of book is not so good....Read more
...My favorite chapter is "Practical methodology" that provided with ways to architect your models from scratch to the end....Read more
The book reads like an academic survey. It has near zero practical value.Read more
...treatment of AI (which is an absolute necessity) and practical implementation (Algorithms, real issues like underflow & overflow....)....Read more
Great book as the introduction in machine learning and deep learning
Received the book shrink wrapped in Amazon box, sold by Globalmart online shop. The books is at a reasonable price and not an international edition. Although most of book content can be accessed from the author's website, it is still better to have a physical copy to flip through since the online material is not in pdf form. This book along with the keynote slides and pdf documents as well as lectures from Goodfellow's website, helps me understand the fundamentals of machine learning and deep learning. The first few chapters cover the linear algebra and probability. I feel those chapters very helpful since I don't have very strong math background. This book doesn't cover too much hands-on stuff. But I think the theory is the bottle neck in ML and DL. I would highly recommend this book and the author who is the industry expert in this field.
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- Reviewed in the United States on April 18, 2017
Top reviews from other countries
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AlbertoFer97Reviewed in Spain on September 17, 2025
Un referente en Deep Learning, complejo pero imprescindible
Format: HardcoverVerified Purchase - AmrReviewed in the United Arab Emirates on May 23, 2019
Great book for Data Scientists
Format: HardcoverVerified Purchase - Mahan GhafariReviewed in the United Kingdom on August 3, 2025
authoritative book
Format: HardcoverVerified Purchase -
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AlbertoReviewed in France on March 2, 2022
Bible du deep learning
Format: HardcoverVerified Purchase
