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.

Key Features

·   Understanding GANs and their potential

·   Hands-on code tutorials to build GAN models

·   Advanced GAN architectures and techniques like Cycle-Consistent Adversarial Networks

·   Handling the progressive growing of GANs

·   Practical applications of GANs

Written for data scientists and data analysts with intermediate Python knowledge. Knowing the basics of deep learning will also be helpful.

About the technology

GANs have already achieved remarkable results that have been thought impossible for artificial systems, such as the ability to generate realistic faces, turn a scribble into a photograph-like image, are turn video footage of a horse into a running zebra. Most importantly, GANs learn quickly without the need for vast troves of painstakingly labeled training data.