Deep Learning with Python, Second Edition

Good, here is how it works information and plenty of programming examples where you create a minimal LLM system. Easy to understand and the author goes to great lengths to ensure you understand what's required. If you are just out of college and want to get into this discipline, this is a book you should understand before you do your first interview. Note that you probably don't have a computer capable of LLM development by yourself. Go price an NVidia A100.

Vey well written and presented. Easy to follow along. I had some background in foundational machine learning and this book has helped me level up into deep learning! I loved how simple it made the theory that you need to understand.

Great book.

Fantastic book. Early chapters explain the history and basics of what goes into an AI system. The author is a great writer and is easy to understand. The book is printed on high-quality paper and is in color. I highly recommend this book if you are starting to learn about AI.

Anyone interested in deep learning needs to read this book. It’s great as an introduction to deep learning but also of value to those more experienced in the field. The author explains concepts in a succinct, brilliant fashion. He is also the creator of Keras, one of the most popular and widely used Python deep learning library.

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

This is a great book to get into deep learning. While PyTorch is the more popular deep learning library today, the fact that it uses Keras is still ok and it is very very well written.

The author's eloquence and energy in conveying his thoughts give the impression that he was speaking directly to the notepad to create the first draft. The content is rich, clear, and easy to grasp. Having taken two Coursera AI courses since 2018, this book promises to connect all the dots for me and enhance my understanding.