In this chapter, we will look at Generative Adversarial Networks (GANs). They are a type of deep neural network architecture that uses unsupervised machine learning to generate data. They were introduced in 2014, in a paper by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, which can be found at the following link: https://arxiv.org/pdf/1406.2661. GANs have many applications, including image generation and drug development.
This chapter will introduce you to the core components of GANs. It will take you through how each component works and the important concepts and technology behind GANs. It will also give you a brief overview of the benefits and drawbacks of using GANs and an insight into certain real-world applications.
The chapter will cover all of these points by exploring the following topics:
- What is a GAN?
- The architecture of a GAN
- Important concepts related to GANs
- Different varieties of GANs
- Advantages and disadvantages of GANs
- Practical applications of GANs
