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 because it is one of the most rapidly growing areas of machine learning (ML). This book will test unsupervised techniques of training neural networks as you build eight 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 in the different projects in the book. With every chapter, the level of complexity and operations advances, helping you get to grips with the GAN domain.

You will cover popular approaches such as 3D-GAN, DCGAN, StackGAN, and CycleGAN, and you'll understand 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 projects.