Book Image

Generative Adversarial Networks Projects

By : Kailash Ahirwar
Book Image

Generative Adversarial Networks Projects

By: Kailash Ahirwar

Overview of this book

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.
Table of Contents (11 chapters)

Introduction to Generative Adversarial Networks

In this chapter, we will look at Generative Adversarial Networks (GANs). They are a type of deep neural network architecture that uses unsupervised machine learning to generate data. They were introduced in 2014, in a paper by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, which can be found at the following link: GANs have many applications, including image generation and drug development.

This chapter will introduce you to the core components of GANs. It will take you through how each component works and the important concepts and technology behind GANs. It will also give you a brief overview of the benefits and drawbacks of using GANs and an insight into certain real-world applications.

The chapter will cover all of these points by exploring the following topics:

  • What is a GAN?
  • The architecture of a GAN
  • Important concepts related to GANs
  • Different varieties of GANs
  • Advantages and disadvantages of GANs
  • Practical applications of GANs