Deep Learning with Python

I have bought 10 books on ML/DL, and of those this is the 9th book that I have read (actually I have just started reading this book, but it's been so good thus far that I wanted to write a review.) As another reviewer noted, one should read other books on ML/DI to get a deeper understanding of the topic. This book explains using programs instead of using much mathematics. The advantage that I have had is my review of the same topics from other perspectives in books such as the following Intro to statistical learning (by Hastie et al) Intro to Machine Learning (by Alpaydin) Deep Learning (by Goodfellow, Bengio etc) Hands-on ML w SciKit, Keras and Tensorflow (by Geron) When I first tried to read this book by Chollet in early April I was not as conversant with Python, and so I took a break and decided to brush up my limited Python knowledge by going through the first 6 chapters of "Automate the Boring Stuff with Python" (by Sweigert). Now that I have more knowledge of Python this book by Chollet is so much more comprehensible. As I said I have the advantage of having learned many of these concepts earlier. I love Chollet's interpretation and explanations. I wish I could do the exercises but am having difficulty setting up the GPU machine. The problem I am dealing with with this book by Chollet is the setup of a GPU machine in the Amazon Cloud. If anyone can help me that would be greatly appreciated (I understand that this is not the forum to seek technical help on AWS, but I thought I'd give it a try)

Coming from a non-data science background (IT networking), data science is an add-on skill to my foundation. I do not need to fully understand all of the mathematical theory - instead I need to know how to use deep learning to develop use-cases. I bought this book to understand what I could do with deep learning in Keras. I got so much more than I expected. Having written a single chapter in my own book about algorithms in general, I understand the challenges of trying to explain algorithms enough for general understanding, while not getting too far down the rabbit hole. I thought this book went to a perfect depth to understand the possibilities with deep learning, and to get hands on creating useful outcomes. Thanks Francois for the time well spent.

Read this cover to cover for my senior project and loved every minute of it, Francois Chollet was somehow able to make a textbook into a page turner, explaining challenging concepts conceptually while giving implementation examples. I also got the second addition and I would recommend using that one just so you are working through up-to-date examples with tensorflow/keras. The field of deep learning is really vast and Chollet covers an impressive amount in this book mostly at a relatively high/applied level, which I think is a good thing. There were a few of the later chapters I wish he went into more depth with, for the advanced computer vision chapter I really which he had touched on some more modern architectures like Mask- RCNN and other stuff

I'm a CS professor, and I chose this for my course in Deep Learning last term. Overall I am happy with the book, and will use it again. It rates 5 (or even 6!) stars for being an approachable introduction to Deep Learning, using the author's excellent Keras library to allow beginners to do remarkable work. My own class of undergrads was building DLNN models to do sophisticated image recognition tasks after just a few weeks. So, why the four stars? Because the book is rather "paint by the numbers". The presentation is filled with "Now you'll do this.." followed by working blocks of code for the student to enter and run. But there are no exercises, code or mathematical. Even the standard backpropagation algorithm is only qualitatively described -- nice pictures of gradient descent in 2 dimensions, but no hard equations. (After all, Keras does it all for you, right?) And as the book ventures into more advanced areas like GANs, VAEs, etc the presentation is increasingly high-level and nonmathematical, providing only a feel for the topics without deep comprehension. Given the depth of the math involved, I suppose I can't blame Chollet for a bit of handwaving. But more rigor with deeper explanations would have been nice.

This is probably the best into to Deep Learning one could get. Author just knows how to speak clearly, give information at the appropriate time, is well structured and still gives some very in dept info. It is limited to deep learner but that’s why its called what it is. The author dabbles in other areas so the reader is aware of other things in AI. Definitely a good starting point for someone with some programming chops but new to AI.

I'm using this as the primary textbook for a Deep Learning course I'm designing right now for the University of Washington professional/continuing education program. I'll also assign readings from the Goodfellow et al. text, but Chollet's book is a more practical way to get started. He is also the author of the Keras framework; it's great to get advice "straight from the horse's mouth". Overall this book is more about practical techniques and python code (in Keras) than about deep learning math/theory. This is probably what the majority of readers are looking for. It's a great synthesis of the most important techniques now (start of 2018), which is hard to get just from reading papers. I would recommend complementing this book with two others: 1) as mentioned above: Deep Learning (Adaptive Computation and Machine Learning series) 2) Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

François Chollet does a phenomenal job explaining the background, fundamentals, and applications of deep learning. His examples in python are comprehensive, but not overly difficult to grasp. To boot, he offers python examples for free as Jupyter notebooks available through Github, and many of these include excellent commentary. My one minor minor gripe is that he goes out of his way to avoid introducing mathematical symbols/nomenclature to make his book more accessible, but I sometimes find explaining things with a little mathematical nomenclature could aid in succinctness without losing the audience.

Francois Chollet has a great talent to explain even complicated things in a very clear and intuitive way. The book has just enough details: touching all important things and at the same time not going too deep and away from the subject. I found it as an excellent addition to Deep Learning specialization from Andrew Ng on Coursera. I read it from cover to cover, practiced with all examples. These examples are well explained, detailed, and self-sufficient. If you want to try something, just do it - as the book provides all the details you need (you don't even need your own environment - just use colab.research.google.com). So, I want to say big Thank You to Francois for doing such great things for the machine learning community!