Generative Adversarial Networks Projects

Explore the power of Generative Adversarial Networks to empower your business

Key Features Use different datasets to build advanced projects in Generative Adversarial Networks domain Implementing projects ranging from generating 3D shapes to Face aging application and so on. Explore the power of GANs to empower your business offerings Book Description

Generative Adversarial Networks (GANs) potential is huge because they can learn to mimic any distribution of data. Major research and development work is going into the field since it is one of the growing areas of machine learning. This book will bring your skills to test while building 8 end-to-end projects in GANs domain.

This book will start with the required concepts, tools/libraries that will be used to build efficient projects, thus giving you the foundation to get well versed with the concepts and principles. We will use different complexities of datasets in order to build end-to-end projects. With every chapter, the level of complexity and operations will become advanced thus providing you the grip over GAN domain.

It consists of 8 projects covering popular approaches such as 3D-GAN, Age-cGAN, DCGAN, SRGAN, StackGAN, TextureGAN and CycleGAN. We will bring the real-world practical implementation of the models to understand the architecture and functioning of generative models.

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

What you will learn Training the network on the 3D Shapenet dataset to generate realistic shapes Learn Keras implementation of DCGAN for anime character generation in Jupyter Notebook Implementing a SRGAN network to generate high-resolution images Training the Age-cGAN on IMDB Wiki Images dataset Understanding of Conditional GAN for image-to-image translation Generator and discriminator implementation of StackGAN in Keras Who This Book Is For

This book is intended for data scientists, machine learning developers, deep learning practitioners and AI enthusiasts who want a project guide to test their knowledge and expertise in building real-world GANs models. These full-fledged projects will help you master machine learning, and neural network principles. Basic understanding of machine learning and deep learning concepts will be handy. Hands-on experience in Tensorflow or Keras will be a plus point

About the Author

Kailash Ahirwar is a machine learning and deep learning enthusiast. He has worked in many areas of Artificial Intelligence (AI)ranging from Natural Language Processing, Computer vision to generative modeling using GANs. He is a co-founder and CTO of Mate Labs. He uses GANs for building different models such as turning painting into photos and Controlling Deep Image Synthesis with Texture Patches, etc.

He is super optimistic about AGI and believes that AI is going to be the workhorse of human evolution