Generative Adversarial Networks (GANs) have brought about a revolutionary storm in the machine learning (ML) community. They, to some extent, have changed the way people solve practical problems in Computer Vision (CV) and Natural Language Processing (NLP). Before we dive right into the storm, let's prepare you with the fundamental insights of GANs.
In this chapter, you will understand the idea behind adversarial learning and the basic components of a GAN model. You will also get a brief understanding on how GANs work and how it can be built with NumPy.
Before we start exploiting the new features in PyTorch, we will first learn to build a simple GAN with NumPy to generate sine signals so that you may have a profound understanding of the mechanism beneath GANs. By the end of this chapter, you may relax a little as we walk you through...
