This block will teach you how to implement GANs (Generative Adversarial Networks) in Machine Learning.

Introduction to GANs

Generative adversarial networks, also known as GANs, are deep generative models. Like most generative models they use a differential function represented by a neural network known as a Generator network. GANs also consist of another neural network called Discriminator network. This module covers everything about the introduction to GANs.

Autoencoders

Autoencoder is a type of neural network where the output layer has the same dimensionality as the input layer. In simpler words, the number of output units in the output layer is equal to the number of input units in the input layer. An autoencoder replicates the input to the output in an unsupervised manner and is sometimes referred to as a replicator neural network.

Deep Convolutional GANs

Deep Convolutional GANs works as both Generator and Discriminator. You will learn how to use Deep Convolutional GANs with an example.

How to train and common challenges in GANs

In this module, you will learn how to train GANs and identify common challenges in GANs.

Semi-supervised GANs

The Semi-Supervised GAN is used to address semi-supervised learning problems.

Practical Application of GANs

In this module, you will learn all the essential and practical applications of GANs.