Title Generative adversarial networks cookbook : over 100 recipes to build generative models using Python, TensorFlow, and Keras / Josh Kalin.

Publication Info.	Birmingham : Packt, [2018]
	©2018

Online access
Online ebook via EBSCO. Access restricted to current Rider University students, faculty, and staff.
Instructions for reading/downloading the EBSCO version of this ebook

Item Status

Description	1 online resource (261 pages)
Bibliography	Includes bibliographical references and index.
Summary	Generative Adversarial Networks have opened up many new possibilities in the machine learning domain. This book is all you need to implement different types of GANs using TensorFlow and Keras, in order to provide optimized and efficient deep learning solutions.
Contents	Table of ContentsWhat is a Generative Adversarial Network? Data First -- How to prepare your datasetMy First GAN in under 100 linesDreaming new Kitchens using DCGANPix2Pix Image-to-Image TranslationStyle Transfering Your image using CycleGANUse Simulated Images to Create Photo Realistic Eyeballs using simGANFrom Image to 3D Models using GANs.
Local Note	eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America
Subject	Machine learning.
	Neural networks (Computer science)
	Python (Computer program language)
	COMPUTERS -- General.
	Machine learning
	Neural networks (Computer science)
	Python (Computer program language)
Other Form:	Print version: Kalin, Josh. Generative Adversarial Networks Cookbook : Over 100 Recipes to Build Generative Models Using Python, TensorFlow, and Keras. Birmingham : Packt Publishing Ltd, ©2018 9781789139907
ISBN	1789139589 (electronic bk.)
	9781789139587 (electronic bk.)