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Deep Learning with Keras: Implementing deep learning models and neural networks with the power of Python Paperback – April 26, 2017
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About the Author
Antonio Gulli is a software executive and business leader with a passion for establishing and managing global technological talent, innovation, and execution. He is an expert in search engines, online services, machine learning, information retrieval, analytics, and cloud computing. So far, he has been lucky enough to gain professional experience in four different countries in Europe and managed people in six different countries in Europe and America. Antonio served as CEO, GM, CTO, VP, director, and site lead in multiple fields spanning from publishing (Elsevier) to consumer internet (Ask and Tiscali) and high-tech R&D (Microsoft and Google).
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Top customer reviews
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This book stands out because it gives details about the implementation aspects of coding many different deep learning models that you will hear about in the literature and in the field. For example, LeNet, ResNet, etc. among many others are demonstrated through out the book.
Generally speaking, topics in deep learning are not easy to explain to the average reader and I think the author recognizes this difficulty and chooses to place his focus on demonstrating how to implement deep learning methods and being careful to explain what the different modules do and their respective parameters.
In my view, this book is very suitable for Data Scientists who already know the spectrum of machine learning models and techniques and want to get their hands dirty as fast as possible with deep learning. This book is a much better practical book for deep learning than the popular book by Aurélien Géron called "Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems". I have looked at many deep learning books and in my view this one did the best job is getting me comfortable with implementing deep learning models on my own.
The one thing that I found the book was lacking is that it's final chapter on AI and reinforcement learning did not seem as thorough and detailed as the other chapters in the book. Having reviewed many books in the area of deep learning, I can honestly say this is probably the best book I have come across so far. However, I came to this book already having a solid understand of deep learning theory.
Have not tried to run the code samples yet.
I was hoping for a bit more. Maybe given that keras sits on top of tensorflow or Theano there is just not as much to say?
This book contains many code examples and log traces showing execution of the code. The code and log traces are formatted like code on a computer screen and look very washed out (print book). Bolder fonts and a little ink would have made this book a lot easier to read. This also gives you an idea what this book is like - screen shots of code followed by screen shots of code execution.