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GANs in Action (Deep learning with Generative Adversarial Networks)
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Product Details
Author:
Jakub Langr, Vladimir Bok
Format:
Paperback
Pages:
276
Publisher:
Manning (October 7, 2019)
Language:
English
ISBN-13:
9781617295560
ISBN-10:
1617295566
Weight:
13.33oz
Dimensions:
7.38" x 9.25" x 0.4"
File:
Eloquence-SimonSchuster_05022026_P10038138_onix30_Complete-20260502.xml
Folder:
Eloquence
List Price:
$49.99
Case Pack:
15
As low as:
$44.99
Publisher Identifier:
P-SS
Discount Code:
G
Pub Discount:
37
Imprint:
Manning
Overview
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.
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.








