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This book provides a good introduction for programmers that are interested in the subject. As the author is kind to leave complex math aside while explaining the concept. This book is a great addition to a programmer's library.
The contents of the book are great but the seller doesn't provide code for online access, which according to the first page of the book, should be there. Not sure if this seller is authorized to sell this book.
Deep learning tutorial, excellently balanced between hands-on examples and deeper concepts explained in an intuitive, non-mathematical way. Very well structured chapters explain step by step the workflow for framing, developing and deploying a real world model. Easy to follow, uses informal language, but with great depth. Highly recommended.
This book is ideally suited to people who want a meaningful introduction into the most important contemporary concepts in Deep Learning. The book is accessible to people who lack both programming and linear algebra. Neither are needed to get a full understanding of everything the book offers.
IMO, the greatest moments in the book are the asides that appear in every chapter. The author will take a paragraph to note in passing things like '... no one really knows for sure why batch normalization helps. There are various hypotheses, but no certitudes." Or, "Importantly, I would generally recommend placing the previous layer's activation after the batch normalization layer (although this is still a subject of debate)." There is even an entire chapter dedicated to musings on the future of Deep Learning and general AI. This is the cherry on top that you don't get with most offers. Chollet offers them in nearly every chapter.
The book may as well have been called "Deep Learning with Keras" and that's not a bad thing. All the code is freely downloadable and can be run for free on a Google platform. You can freely ignore the implementation details and Python and simply run and learn from the notebooks provided. NOTE: As of February 2022, the new M1 Macs have bugs in the implementation of tensorflow that prevent a few code samples from working correctly. AND, some examples take so long to run (many hours) that there may be issues running them at Google. Frustrating though it might be, it does not detract from the experience.
As to cons, I don't see enough to warrant taking a star off the review. All important concepts are covered at an introductory level. The code works. The writing is clear. The author is an expert. There is a bizarre convention of having diagrams flow from the bottom to the top instead of top-down.
It's a good intro and basic reference. You'll get into more depth by taking the OpenAI courses at Coursera, but I'd actually recommend those as a next step after fully absorbing this book. Recommended.
While the book is titled "Deep Learning with Python", it might have been better titled, "Deep Learning with Keras." While Python is ostensibly