Ahirwar - Generative Adversarial Networks Projects : Build Next-Generation Generative Models Using TensorFlow and Keras.

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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 and computer vision to generative modeling using GANs. He is a co-founder and CTO of Mate Labs. He uses GANs to build different models, such as turning paintings into photos and controlling deep image synthesis with texture patches.

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

Jalaj Thanaki is an experienced data scientist with a demonstrated history of working in the information technology, publishing, and finance industries. She is author of Python Natural Language Processing and Machine Learning Solutions, by Packt Publishing.

Her research interest lies in natural language processing, machine learning, deep learning, and big data analytics. Besides being a data scientist, Jalaj is also a social activist, traveler, and nature lover.

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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.

If you're a data scientist, ML 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.

, Introduction to Generative Adversarial Networks , starts with the concepts of GANs. Readers will learn what a discriminator is, what a generator is, and what Game Theory is. The next few topics will cover the architecture of a generator, the architecture of a discriminator, objective functions for generators and discriminators, training algorithms for GANs, KullbackLeibler and JensenShannon Divergence, evaluation matrices for GANs, different problems with GANs, the problems of vanishing and exploding gradients, Nash equilibrium, batch normalization, and regularization in GANs.