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Book details  
File Size: 12 MB 
Format: epub 
Print
Length: 316 pages 
Publisher: Packt Publishing; 1 edition (January 31, 2019) 
Publication Date: January 31,
2019 
Sold by: Amazon Digital Services LLC 
Language: English 
ASIN: B07F2MY1QH 

Who this book
is for 
If you’re a data scientist, machine learning developer, deep learning practitioner, or AI enthusiast
looking for a project guide to test your knowledge and expertise in building real-world GANs models, this book is for
you. 

Explore various Generative Adversarial Network architectures using the Python ecosystem 

Key
Features 

Use different datasets to build advanced projects in the Generative Adversarial Network domain 
Implement projects ranging from generating 3D shapes to a face aging application 
Explore the power of GANs to
contribute in open source research and projects 
Book Description 
Generative Adversarial Networks (GANs) have
the potential to build next-generation models, as they can mimic any distribution of data. Major research and
development work is being undertaken in this field since it is one of the rapidly growing areas of machine learning.
This book will test unsupervised techniques for training neural networks as you build seven end-to-end projects in the
GAN domain. 
Generative Adversarial Network Projects begins by covering the concepts, tools, and libraries that you
will use to build efficient projects. You will also use a variety of datasets for the different projects covered in the
book. The level of complexity of the operations required increases with every chapter, helping you get to grips with
using GANs. You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you’ll gain an
understanding of the architecture and functioning of generative models through their practical implementation. 

By the end of this book, you will be ready to build, train, and optimize your own end-to-end GAN models at work or in
your own projects. 

What you will learn 

Train a network on the 3D ShapeNet dataset to generate
realistic shapes 
Generate anime characters using the Keras implementation of DCGAN 
Implement an SRGAN
network to generate high-resolution images 
Train Age-cGAN on Wiki-Cropped images to improve face verification 
Use Conditional GANs for image-to-image translation 
Understand the generator and discriminator implementations
of StackGAN in Keras 
Table of Contents 

Introduction to Generative Adversarial Networks 
3D-GAN
– Generating Shapes Using GANs 
Face Aging Using Conditional GAN 
Generating Anime Characters Using DCGANs

Using SRGANs to Generate Photo-Realistic Images 
StackGAN- Text to Photo-Realistic Image Synthesis 
CycleGAN- Turn Paintings into Photos 
Conditional GAN – Image-to-Image Translation Using Conditional
Adversarial Networks 
Predicting the Future of GANs

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