Deep Learning (Adaptive Computation and Machine Learning series)

Very deep and high technical quality
Really deep to learn DL and the foundations of modern IA, from ML to the new paradigm of transformers, the book itself doesn’t explain transformers but it gives you a strong foundation on math and the algorithms used by the new tech era

Excellent book, possibly currently unique in coverage of latest ideas
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.

The definitive guide to becoming a researcher in the field
This is not a coding book. I see a lot of negative reviews around the expectation that this book would teach the reader how to quickly build machine learning systems and write code. This book is not for that audience.If you just want to build applications, don’t worry about how deep learning works. It’s akin to needing to understand how an engine works just to drive a car. If you are looking for a coding resource, try: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_4?keywords=machine+learning+tensorflow&qid=1579608765&sr=8-4 . And even with that book, the material still goes far beyond what you need – use it as a light reference.I bought this book as an aspiring machine learning researcher, and towards that end, it is the best resource available in print (still true as of 2020). For instance:The first 5 chapters are timeless. These are things that were mostly established 20 or 30 years ago and beyond and are mostly STEM fundamentals at this point. There are whole textbooks dedicated to each of those chapters, but the authors provide a quick refresher and overview of probably 80% of what you’ll encounter in deep learning. If you haven’t previously learned each of these subtopics, you’ll probably want to study them individually since they are the key to innovating (linear algebra, probability & stats, numerical computation, machine learning fundamentals).Chapters 6 thru 9 are the foundation of deep learning. We’re about 12 years into seeing rapid change in the deep learning space, yet all of these principles and techniques still hold (many recent innovations are still relying on Convolutional models in 2020, which is the most layered/complex topics in those chapters). Therefore, I’d wager that these chapters are also fairly stable knowledge that is worth internalizing if you want to be deeply involved in the future of machine learning.Chapters after 9 are mostly experimental topics, and many of them are already the wrong strategies for optimal results. But there are interesting ideas in here that you’ll often encounter in the wild, so it’s good exposure to various topics. But probably not worth much of your time.And lastly, there is good history in here from people who know the space intimately. It’s a good way to piece together the developments and learn the lexicon of deep learning so you can have intelligent conversation with experts.

Great book for aspiring deep learning enthusiasts

Las bases del aprendizaje profundo están en este libro, un buen conocimiento en matemáticas avanzadas es crucial