Fundamentals of Machine Learning

With continuously evolving research and development, generative adversarial networks (GANs) are the next big thing in deep learning. This module highlights the key improvements in GANs over traditional generative models and shows us how to make the best out of GANs with the help of hands-on examples.

This module will help us understand how GAN architecture works using PyTorch. We will familiarize ourselves with the most flexible deep learning toolkit and use it to transform ideas into actual working code. We will apply GAN models to areas such as computer vision, multimedia, and natural language processing using a sample-generation methodology.

We will use a sample-generation methodology to apply GAN models to areas such as computer vision, multimedia, and natural language processing.

GANs have created a revolutionary storm in the machine learning (ML) community. To some extent, they have changed how people solve practical problems in computer vision (CV) and natural language processing (NLP). Before we dive right into the storm, let us prepare ourselves with the fundamental insights of GANs. 

How GANs work—an analogy

To introduce how GANs work, let’s use an analogy:

A long, long time ago, there were two neighboring kingdoms on an island. One was called Netland, and the other was called Ganland. Both kingdoms ...