Deep Learning with Python, Second Edition

I have more than 50 books on AI & ML, but Francois Chollet's bible on DL (first edition) is the only one which is open on my desk since 2018.
The author decision to focus on working Python code instead of the math behind it - was crucial for its amazing success.
As others have mentioned - Chollet is an excellent teacher which can explain complex and complicated ideas to the masses. I have used his code and DL techniques successfully in many projects, hackathons and Kaggle competitions.
I've just received my second edition a couple of days ago - and the book is about 50% bigger than the first edition, as the author has added more examples and details plus full color printing.
Strongly recommended to any aspiring data scientist.

The content of this book is 5 stars. It is a great book with intuitive explanations and plentiful examples.

However, the seller Sacred Gamez is printing counterfeit copies from a non-final draft.

In the chapter titled "About This Book" in the "Who should read this book" section it states that "You don't need previous experience with machine learning or deep learning: this book covers, from scratch, all the necesary basics." After reading chapter two I was so confused (and I consider myself an early intermediate of python and deep learning). I can only imagine how confusing this would be to a beginner of deep learning, and also a beginner of python. The section explaining tensors appears to me to have been written for someone who is already experienced and knolwedgeable on the subject. Ok, the book does say that it is written for people with python programming experience but it does say that you don't need previous experience with deep learning. So you do need python experience to read the book, but in my opinion you most definitely need exposure and experience in deep learning concepts to understand the concepts and you need to have a great understanding of tensors before you read the book. In my opinion the code is poorly explained in many sections for an intermediate python person like myself and a beginner would be completely lost. They seem to be trying to make a neural network from scratch in chapter 2 and it was extremely difficult for me to follow as an intermediate python coder. I just do not understand why authors don't take the time to explain the code carefully, completely and slowly. It appears to me that this book makes alot of assumptions about the reader's knowledge of dep learning. Comparing this book to the book called Artificial Intelligence and Deep Learning with Python: Every Line of Code Explained For Readers New to AI and New to Python, shows where Deep Learning with Python is lacking. Artificial Intelligence and Deep Learning with Python Every Line of Code Explained is a refreshing contrast since each line of code is fully and sometimes overly explained. If I had a choice between the two there would be no contest. I would rather have a book that "overlly" explains concepts and the code than a book that "under-explains" the code and concepts. And if you are a beginner of either python or deep learning, you should keep away from this book all together and go for the other book I mentioned, where both concepts and code are explained much better and much easier to understand. I received the Deep Learning with Python, Second Edition book as a gift so that is why I don't have a verified purchaser designation on my review.
An update. I finished reading the book and I am very disappointed. The chapters on CNNs and RNNs is so difficult and confusing. If you are new to deep learning you will be lost. The author should have given more simple and clearer examples. And why isn't every line of code explained?? Take it from me , if you are new to neural networks and deep learning check out Artificial Intelligence and deep learning with python: every line of code explained. It is a far superior book for those new to deep learning and neural networks. And if you're new to python you will be able to understand it infinitely better than this book.

Why such a huge price difference between Amazon and Manning ($32.99)? Although I have only read the 1st edition, and it was fantastic, I'm sure 2nd edition is even better given the author's stellar reputation. Match the price Amazon and I'll buy it.

The printing looks like the printer is on super super ink saver mode. Hurts my eyes trying to read it. The letters are gray not black. Attaching a photo of this book next to a regular book.

Many pages are not cut. I had to separate the pages myself.

Incredibly well presented material that conveys the author's (obviously extensive) knowledge to the Keras layperson (though a bit of Python experience is presumed). This content is approachable for the Machine Learning neophyte without being overwhelming. A beautifully-delivered and finely tuned set of reasoned funnels that make it feel as though the reader, him/herself has concluded what the correct path should be, because "it's obvious" as you continue reading. I have many, many Machine Learning books. This one is the first one that - after reading a paragraph, I say "But, of COURSE!" Brilliantly presented and deftly navigated. Bravo!

I pre-ordered the 2nd Edition because I loved the 1st Edition so much. I am still in the midst of working through the book, and I am finding it extremely useful, informative, interesting, and insightful. It is simply one of the best resources I have come across on this subject. Period. Kudos to Francois Chollet for his contribution to democratizing the development and deployment of AI technology. He is truly an amazing teacher as well!

Fantastic book especially if you have taken at least the first course in Andrew Ng's "Machine Learning Specialization" on Coursera. Andrew Ng lead the team that created TensorFlow and Francois Chollet is the creator of the Keras API that is used to interface with TensorFlow 2.0. Both the new version of Andrew Ng's course and the second edition of this book use Python and the TensorFlow 2.0 package.

I am a beginner to Machine Learning (ML), but have programming experience. I wanted to educate myself on ML so that I can eventually write apps that leverage ML. I purchased a number of books on Machine Learning using Python and hands down the best of the bunch is "Deep Learning with Python" by the Zen master himself, Francois Chollet. This is a no-nonsense book that explains concepts in a clear and concise manner, using concrete examples. The author does not water down his discussions nor does he go into gory mathematical details. Rather, he uses coding examples to explain concepts. If you are just starting out, this book has to be the first one you own. I will caveat my review by saying that the author is fond of Tensorflow and this is reflected in the book. Still, the concepts explained in this book do apply to ML in general.

This book is fantastic! Extremely well written, easy to follow for anyone with some programming experience. Francois is a master in the field and the book is accessible, up to date, and an excellent way to jumpstart a career in machine learning!