Deep Learning with Python, Second Edition

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

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.

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.

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.

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.

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 is by far the best technical book you'll ever buy - that's a hill I'll defend for a long time. The author is one of the rare people that have deep knowledge and a gift for simplifying and communicating the concepts. I follow the code examples in this book for each and every chapter - they worked flawlessly, the code itself is listed within the book and each important line is explained with a sidebar. If you are starting off with deep learning - this is THE book you need.

The book is very educational and helpful. Manning Publications promises free downloads but the registration process is broken and thus no downloads. In order to register, there is supposed to be codes in the book on certain pages. My book does not have these codes and the publisher would not answer my emails. I tried to communicate again this year and the response was immediate. After sending photos of the book, my registration process was completed and I now have access to the Python source files, the E-book second edition, and an E-book first edition.

The quality of the book itself is really good. I also love the content.

It's a good book and reads well. It could use some formatting changes to make some of the content more digestible. But overall a great book.

Uno dei migliori libri in commercio di macchine learning, in particolare sul deep learning usando python e tensor flow.

Es un libro excelente, el autor explica conceptos complicados de una forma sencilla y entendible. Realmente hizo un gran trabajo de pedagogo, además estás aprendiendo del mismísimo autor de Keras, el framework más popular para machine learning. Eso sí, es importante tener conocimiento de programación y de conceptos matemáticos (cálculo, geometría, derivación, etc) ya que el Deep Learning es básicamente eso, pura matemática; vectores, matrices, operaciones vectoriales, espacios geométricos en varias dimensiones, etc. Cabe aclarar que el libro NO usa notaciones matemáticas; para darle sencillez, el autor decide usar en su lugar líneas de código que lo hacen mucho más digerible. Sin embargo, tener el conocimiento de estos conceptos te da el poder de entender lo que se está haciendo y de lo que se está hablando. PD: el libro en físico incluye todas las versiones digitales! Incluso Kindle!

Good book for Deep learning but one should know the basic knowledge of Numpy, Pandas and Data Visulaization and Machine Learning.