Understanding Deep Learning
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An authoritative, accessible, and up-to-date treatment of deep learning that strikes a pragmatic middle ground between theory and practice.
Deep learning is a fast-moving field with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and up-to-date treatment of the subject, covering all the key topics along with recent advances and cutting-edge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, self-contained textbook suitable for anyone with a basic background in applied mathematics.
Up-to-date treatment of deep learning covers cutting-edge topics not found in existing texts, such as transformers and diffusion modelsShort, focused chapters progress in complexity, easing students into difficult concepts Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of modelsStreamlined presentation separates critical ideas from background context and extraneous detailMinimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible Programming exercises offered in accompanying Python Notebooks
Publisher : The MIT Press
Publication date : December 5, 2023
Language : English
Print length : 544 pages
ISBN-10 : 0262048647
ISBN-13 : 978-0262048644
Item Weight : 2.89 pounds
Dimensions : 8.27 x 1.46 x 9.29 inches
Best Sellers Rank: #59,043 in Books (See Top 100 in Books) #24 in Computer Neural Networks #106 in Internet & Social Media #147 in Artificial Intelligence & Semantics
Customer Reviews: 4.9 4.9 out of 5 stars (197) var dpAcrHasRegisteredArcLinkClickAction; P.when(‘A’, ‘ready’).execute(function(A) { if (dpAcrHasRegisteredArcLinkClickAction !== true) { dpAcrHasRegisteredArcLinkClickAction = true; A.declarative( ‘acrLink-click-metrics’, ‘click’, { “allowLinkDefault”: true }, function (event) { if (window.ue) { ue.count(“acrLinkClickCount”, (ue.count(“acrLinkClickCount”) || 0) + 1); } } ); } }); P.when(‘A’, ‘cf’).execute(function(A) { A.declarative(‘acrStarsLink-click-metrics’, ‘click’, { “allowLinkDefault” : true }, function(event){ if(window.ue) { ue.count(“acrStarsLinkWithPopoverClickCount”, (ue.count(“acrStarsLinkWithPopoverClickCount”) || 0) + 1); } }); });
13 reviews for Understanding Deep Learning
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Original price was: $100.00.$89.98Current price is: $89.98.
Evgeny Adamenkov –
Amazing introduction to modern deep learning
Absolutely amazing book. Very clear exposition. Has exercises to solidify understanding. Includes transformers, GANs, VAEs and what not. Much recommended.
Stevan Cakic –
Great book, great content
Great content, great balance between theory and practice, great figures, very nice style of writing.I used this book for my classes, and I have to say that it is the best book I have used so far.
Andrew –
The ‘Bible’ of modern deep machine learning
Excellent book. Lots of diagrams. Appropriate for anyone interested in deep machine learning. Would require some background in mathematics (calculus/linear algebra).
Darrell Knape –
I love this book.
The is one of the best text books ever. It’s incredibly well structured with great diagrams and explanations.
Zain Khandwala –
Impressive
Irrespective of how much experience you have in the field, you’d likely learn a thing or two from this work. It’s a very thoughtful exposition of the fundamentals of deep learning, approaching core topics from multiple perspectives, while balancing the need to delve into the math of it all and remaining approachable. As others have mentioned, the material in the later chapters, on topics of current interest such as transformers, isn’t as strong as the foundational chapters, but it’s still useful. I’ll return to this book often and look forward to any future editions…
Marcus Frödin –
Thorough, easy to understand and good balance between theory and practice
I have a prior, basic understanding of ML as well as an engineering background, and with that I found the mix of theory and practice in building up understanding of modern deep learning systems easy to grok. The author builds up to more advanced and modern concepts (such as diffusion models, and flows) in a natural way, and manages to synthesize research that is if not state of the art then at least not that many years behind into something understandable for a working engineer without a PhD. Overall would highly recommend.
jude –
Excellent resources for all levels
I recently finished my masters in bioengineering and while i learned a lot of mathermaticalprinciples and some novel computational modeling skills, we did not touch on deep learning. This book has been just an excellent resource in building this foundation – granted it does help to have a background in multi variate calculus and statistics, but this book tells you everything you need to know.The writing style is easy to follow and the flow of each chapter is intuitive. The best book on DL to cut through the noise! Also recommend going through the python notebooks. The author was kind enough to send me the answers to the notebook as i’m just self studying not in a class.Looking forward to the next addition!
Aaron –
A worthwhile investment
This book is amazing. I come from a software engineering background and the practice (writing code and using models) is very easy for me. I wanted to learn all the technical jargon and terminology, math and concepts behind deep learning so that I could build anything I wanted from scratch. Although I just started reading it, this book seems perfect for that. I read about linear regression, loss functions and fundamentals thus far and I could write these things up any language I know now because the book helped me understand the concepts properly.
Amazon Customer –
Great book that I use as a supplemental material to get a deeper understanding of various ML topics.
Roozbeh Valavi –
This is an excellent book on deep learning. The explanations are clear and well-structured, and the numerous visualisations really help make complex ideas easier to understand. It strikes a great balance between being accessible and technically solid, making it a great choice for newcomers and others.The book arrived on time and in very good condition. Highly recommended!
Aditya Nigam –
Super book.
Cliente Amazon –
This is easily the most updated and clearly written theoretical book on Deep Learning. The author cuts straight to the point with great clarity, and modern topics are carefully selected to give a complete overview of the field. In several parts of the book the author makes recent developments in DL accessible to a larger audience, presenting cutting edge research in a dense yet very enjoyable manner.
Eduardo Hiroshi Nakamura –
Infelizmente com exemplos em Python.