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Gans in Action Deep Learning with Generative Adversarial Networks

Gans in Action Deep Learning with Generative Adversarial Networks

Gans in Action Deep Learning with Generative Adversarial Networks Paperback - 2019

by Langr, Jakub & Vladimir Bok

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Shelter Island, NY: Manning. Very Good-. 2019. 1st Edition (Unstated). Paperback. 7.38 X 0.4 X 9.25 inches; 214 pages; Light rubbing (shelf wear) to covers. Very Good condition otherwise. No other noteworthy defects. No markings. ; - Your satisfaction is our priority. We offer free returns and respond promptly to all inquiries. Your item will be carefully cushioned in bubble wrap and securely boxed. All orders ship on the same or next business day. Buy with confidence. .
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Details

  • Title Gans in Action Deep Learning with Generative Adversarial Networks
  • Author Langr, Jakub & Vladimir Bok
  • Binding Paperback
  • Edition 1st Edition (Unstated)
  • Condition Used - Very Good-
  • Pages 276
  • Volumes 1
  • Language ENG
  • Publisher Manning, Shelter Island, NY
  • Publication date 2019
  • Bookseller's Inventory # HVD-45076-OS-0
  • ISBN 9781617295560 / 1617295566
  • Weight 0.9 lbs (0.41 kg)
  • Dimensions 9.2 x 7.4 x 0.4 in (23.37 x 18.80 x 1.02 cm)
  • Category Computers - Communications / Networking

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Reader reviews for Gans in Action Deep Learning with Generative Adversarial Networks

From the publisher

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.

About the author

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.

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.

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