Get Free Ebook Deep Learning with Python
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Deep Learning with Python
Get Free Ebook Deep Learning with Python
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About the Author
Francois Chollet is the author of Keras, one of the most widely used libraries for deep learning in Python. He has been working with deep neural networks since 2012. Francois is currently doing deep learning research at Google. He blogs about deep learning at blog.keras.io.
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Product details
Paperback: 384 pages
Publisher: Manning Publications; 1 edition (December 22, 2017)
Language: English
ISBN-10: 9781617294433
ISBN-13: 978-1617294433
ASIN: 1617294438
Product Dimensions:
7.4 x 0.8 x 9.2 inches
Shipping Weight: 1.4 pounds (View shipping rates and policies)
Average Customer Review:
4.6 out of 5 stars
93 customer reviews
Amazon Best Sellers Rank:
#2,036 in Books (See Top 100 in Books)
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.
I have taken the machine learning class in Coursera but the first two chapters in this book brough a whole new level of clarity to all the concepts. I finally really get what each of the parts of the training and optimization do. Also love the explanations in code instead of in mathematics. I feel I have a much better intuition about vectors than I did before.
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
After completing deeplearning.ai courses on coursera.com, I purchased this book to gain a better understanding of Keras. Keras was used in the courses, but wasn't explained so well. The author provides that explanation but also adds his perspective on neural networks and valuable insights and historical context. I don't think you get a depth of understanding for neural networks from the book. But if you already explored the field of deep learning, this is a great book to help take your exploration to the next level. I am able to use Keras more effectively to quickly try different architectures. It's great book and worth the spend.
I cannot recommend this book highly enough. I have Geron's book on machine learning which is good but I was looking for an explanation of what is under the covers behind the python functions in tensorflow. Chollet, the author of this book, provides an excellent tutorial on the basics. He breaks down complex algorithms involving tensors to the many underlying simple calculations. I like the way he uses python notation to explain the mathematical constructs and operations rather than subscript indices found in most books. Explanations are aided by effective conceptual diagrams. I also like the way he advises when sections can be skipped if the reader has familiarity with specific topics. I find the writing highly readable.
Just finished the first three chapters of this book and you can really feel the enthusiasm of the author. He put so much effort in making the book comprehensible. For example, he doesn't use math equations to explain the theory of neural network but turn to Python code instead. It proves way easier to understand for me, someone working in industry for years. He begins by going straight into our first neural network, stating that "we have to start somewhere", which is a very good philosophy. During this "going straight" process, he knows exactly when I, as a beginner, will get puzzled and always put hints at the right place in the book, telling me not to worry if I don't something. He also uses a lot of metaphors to express concepts, making it fun to read but without loss of accuracy.This book is up-to-date and it is a masterpiece.Will update this review as I read through the book.
If you have taken some deep learning classes on Coursera, such as deeplearning.ai or fast.ai class, this book will serve as a refresher and a good tutorial to implement ideas in Keras. While it does not provide deep theoretical concepts, it explains enough to give you an understanding of what each layer does (conv1D, conv2D, LSTM, GRU, Dense, etc.) It also teaches about different ways to assemble the networks. I especially like the chapter that talks about the functional API, where you can have multiple inputs, and multiple outputs, and layer weight sharing. Most of the other books I read only talked about Sequential models. This book is not for you, if you are looking for mathematical explanations. It's perfect for someone who is not too interested in equations, and just want to have practical understanding.
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