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Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

(7 customer reviews)

$19.79

Used – Like new. Generally, this may mean that there is minor wear from reading and use, but all pages are intact, and the cover is intact. Do expect signs of wear though!

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ASIN : B078N8RDCP Publisher : Packt Publishing; 1st edition (October 31, 2018) Publication date : October 31, 2018 Language: : English File size : 40970 KB Text-to-Speech : Enabled Enhanced typesetting : Enabled X-Ray : Not Enabled Word Wise : Not Enabled Print length : 368 pages Lending : Not Enabled

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7 reviews

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  1. Reilley Dubuc

    Reilley Dubuc

    I have been through more than a couple books on Artificial Intelligence and I find this to be the best. It tackles difficult topics in a clear and concise way that is easy for the reader to understand and follow. The code listings are straightforward. Whether you are a seasoned programmer or just start out, it has something to offer for everyone.

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  2. Natalie Foreman

    Natalie Foreman

    The book provides a good balance of discussions, theory, diagrams and practical code implementations in Keras in many aspects of deep learning. The kind of book that every practitioner in deep learning should have. The chapters on GAN and VAE have been well-explained.

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  3. Raven Barber

    Raven Barber

    This book is a good blend of code, mathematics and explanations.

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  4. Samuel Adkins

    Samuel Adkins

    A unique book for practical applications in Deep Learning. As all too often, deep learning books have provided only a historical snapshot of basic practices. However, Dr. Atienza’s book embraces a more advanced goal of facilitating practical applications based on the latest capability. Thereby, fulfilling a critical knowledge gap for the community Meanwhile, the author is a definitive research leader in the areas of GANs and Auto-encoders. As such, his survey of the current state of the art in these sub-areas of deep learning, is truly invaluable. For example, specific topics that I encountered for the first time reading this book include advanced methods of: Improved and Disentangled GANs. Finally, the book ends with a quite timely discussion of Policy Gradient methods. A current area of strong interest to both the ML research communities Overall, this is a highly excellent book and a unique reference resource for building the applications of GANs, the current state of the art in autoencoders, and those methods of Reinforcement Learning (w/ policy methods). I recommend this book quite highly.

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  5. Alexis Cox

    Alexis Cox

    Advanced Deep Learning with Keras covers a wide breadth of topics and serves as an intermediate entry point into more advanced deep learning models such as RNN’s and GANs. The book provides a good mix of math, diagrams and practical code examples for each topic.

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  6. Wrynn Boucher

    Wrynn Boucher

    I am delighted to write this review. The author Rowel Atienza was my PhD student at the Australian National University where I was a Professor. Rowel was an outstanding student who conducted novel work in human-robot interactionIt is tremendous to see Rowel go on to become a leading researcher in AI This book emphasises a in-depth and practical understanding of one the hottest technologies on the planet – Deep Learning This book covers the latest developments in deep learning such as Generative Adversarial Networks, Variational Autoencoders and Reinforcement Learning (DRL) A key strength of this textbook is the practical aspects of the book. Readers will learn how to implement modern AI using Keras, an open-source deep learning library Finally, and most importantly this book is well written and easy to learn with Well done Rowel!

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  7. Natalie Caporal

    Natalie Caporal

    I am glad to write my review for this textbook. Personally, I think everyone who loves Deep Learning and uses Keras in their day to day lives should have this textbook in their libraries. I have yet read some chapters of this book and have loved it. The author has done an amazing job in explaining the concepts well. I have been specially wanting some good resources to brush up my Variational Autoencoders and GANs concepts and this book has explained them pretty well.

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SKU: B078N8RDCP Category: