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GANs in Action: Deep learning with Generative Adversarial Networks 1st Edition, Kindle Edition

by Vladimir Bok (Author), Jakub Langr (Author) Format: Kindle Edition
4.3 4.3 out of 5 stars 42 ratings
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Deep learning systems have gotten really great at identifying patterns in text, images, and video. But applications that create realistic images, natural sentences and paragraphs, or native-quality translations have proven elusive. Generative Adversarial Networks, or GANs, offer a promising solution to these challenges by pairing two competing neural networks' one that generates content and the other that rejects samples that are of poor quality.

GANs in Action: Deep learning with Generative Adversarial Networks teaches you how to build and train your own generative adversarial networks. First, you'll get an introduction to generative modelling and how GANs work, along with an overview of their potential uses. Then, you'll start building your own simple adversarial system, as you explore the foundation of GAN architecture: the generator and discriminator networks.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
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  1. ISBN-13
    978-1617295560
  2. Edition
    1st
  3. Sticky notes
    On Kindle Scribe
  4. Publisher
    Manning
  5. Publication date
    September 9, 2019
  6. Language
    English
  7. File size
    7396 KB
  8. See all details
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Editorial Reviews

Review

Editorial Reviews "Strikes that rare balance between an applied programming book, an academic book heavy on theory, and a conversational blog post on machine learning techniques." --Dr. Erik Sapper, California Polytechnic State University "Comprehensive and in-depth coverage of the future of AI." --Simeon Leyzerzon, Excelsior Software "An incredibly useful mix of practical and academic information." --Dana Robinson, The HDF Group "A great systematization of the rapidly evolving and vast GAN landscape." --Grigory V. Sapunov, Intento "Excellent writing combined with easy-to-grasp mathematical explanations." --Bachir Chihani, C3

About the Author

Vladimir Bok is a Senior Product Manager at Intent Media, a data science company for leading travel sites, where he helps oversee the company's Machine Learning research and infrastructure teams. Prior to that, he was a Program Manager at Microsoft. Vladimir graduated Cum Laude with a degree in Computer Science from Harvard University. He has worked as a software engineer at early stage FinTech companies, including one founded by PayPal co-founder Max Levchin, and as a Data Scientist at a Y Combinator startup.

Jakub Langr graduated from Oxford University where he also taught at OU Computing Services. He has worked in data science since 2013, most recently as a data science Tech Lead at Filtered.com and as a data science consultant at Mudano. Jakub also designed and teaches Data Science courses at the University of Birmingham and is a fellow of the Royal Statistical Society.

Product details

  • ASIN ‏ : ‎ B09781DYVP
  • Publisher ‏ : ‎ Manning; 1st edition (September 9, 2019)
  • Publication date ‏ : ‎ September 9, 2019
  • Language ‏ : ‎ English
  • File size ‏ : ‎ 7396 KB
  • Text-to-Speech ‏ : ‎ Enabled
  • Screen Reader ‏ : ‎ Supported
  • Enhanced typesetting ‏ : ‎ Enabled
  • X-Ray ‏ : ‎ Not Enabled
  • Word Wise ‏ : ‎ Not Enabled
  • Sticky notes ‏ : ‎ On Kindle Scribe
  • Print length ‏ : ‎ 452 pages
  • Customer Reviews:
    4.3 4.3 out of 5 stars 42 ratings

About the authors

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  1. Vladimir Bok

    Vladimir Bok

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    Vladimir Bok is a Senior Product Manager at Intent, a data science company for leading travel sites, where he helps oversee the company’s Machine Learning research and infrastructure teams. Prior to that, he was a Program Manager at Microsoft. Vladimir graduated Cum Laude with a degree in Computer Science from Harvard University. He has worked as a software engineer at early stage FinTech companies, including one founded by PayPal co-founder Max Levchin, and as a Data Scientist at a Y Combinator startup.

    See more on the author's page
  2. Jakub Langr

    Jakub Langr

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    Full content visible, double tap to read brief content.

    Discover more of the author’s books, see similar authors, read author blogs and more

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Customer reviews

4.3 out of 5 stars
4.3 out of 5
42 global ratings

Top reviews from the United States

Amazon Customer s
5.0 out of 5 stars Fantastic technical overview of an important and growing area
Reviewed in the United States on October 21, 2019
Verified Purchase
5/5. Very impressed by the mix of technical detail and real world application of an emergent area in deep learning.
One person found this helpful
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William R. Buchanan
3.0 out of 5 stars Fairly Narrow in Scope
Reviewed in the United States on September 23, 2022
Verified Purchase
Yes the book addresses GANs. However, it is more heavily focused on computer vision applications with less discussion about text related generative modeling and hardly any discussion related to structured data and/or time series applications. It is definitely a solid text, but would be even better if it addressed broader applications of GAN.
3 people found this helpful
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Lawrence LaPointe
3.0 out of 5 stars Practical but a little light where it matters
Reviewed in the United States on August 26, 2020
Verified Purchase
I'm currently reviewing a couple books in the space. Some are better than others. This, like most, uses the usual data sets with examples to highlight the key aspects of design. I found the parts of the book that dived into the theory a bit frustrating... the authors clearly understand the theory behind e.g. latent space structures but the explanatory math was too minimalist to actually support the explanations. Which was too bad because their enthusiasm for the science behind the code was enticing. Ultimately, I had to switch to other material to really understand what the authors were excited about. Great subject, however, and I am ultimately happy that I read this book as part of the bigger picture.
6 people found this helpful
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Stevano
1.0 out of 5 stars no motivation and the book gets worse in later chapters
Reviewed in the United States on November 4, 2019
The book is too vague and does not explain in the real mathematical idea behind the constructions. A lot of time it just writes the model layers without real mathematical motivation. Also, the quality of the book got progressively worse as you go into later chapters. I usually get a book to get insights that do not exists online and in the case of this book, it does not really have insights--all it has is a collection of codes without any real explanation for the meaning behind it.
14 people found this helpful
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Christ
1.0 out of 5 stars Do not provide enough explanation; Just codes
Reviewed in the United States on January 28, 2020
The authors did give enough explanation of codes and algorithms. For example, when it comes to KL divergency, the authors didn't give formula. When it comes to variational autoencoder, it didn't give prediction based on testing dataset. Avoid as you can.
7 people found this helpful
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Top reviews from other countries

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isaac
5.0 out of 5 stars Qualidade
Reviewed in Brazil on August 22, 2020
Verified Purchase
Muito bom
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S C
1.0 out of 5 stars Not practical
Reviewed in the United Kingdom on January 26, 2021
Verified Purchase
This book is quite dry to read, lack good explanation on the theory. The models and examples use toy dataset which aren't very useful at all.

The first few chapters introducing the basics GANs are okay but you can find similar materials freely available on internet. Most of the code are taken from somebody else's Github repo, perhaps why there lacks in-depth explanation on why the models were constructed in such a way. As already mentioned by others, the later chapters get worse. There wasn't even model implementation for ProgressiveGAN but simply call APIs from TensorFlow Hub.

I found Foster's "Generative Deep Learning" to be easier to read. It is good for simple models up to CycleGAN but it too is lacking in advanced GANs as it dedicates only about half of the book to GANs and the rest are RNN and RL. The best and most complete book on GANs on market now is Cheong's "Hands-on Image Generation with TensorFlow". Not only it is easy to read but also cover all the important techniques leading to state-of-the-art models like StyleGAN to generate photorealistic faces. This is the book if you are serious about learning GANs for practical datasets.
2 people found this helpful
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