Generative Deep Learning with Python

Chapter 3: Deep Dive into Generative Adversarial Networks (GANs)

 

In the previous chapter, we introduced generative models and briefly discussed various types of these models, including Generative Adversarial Networks (GANs). In this chapter, we will delve deeper into GANs and explore their architecture and training process in greater detail. We will also discuss the strengths and limitations of GANs and explore various applications that utilize these models, ranging from image synthesis to drug discovery.

To begin, GANs were introduced by Ian Goodfellow and his colleagues in 2014, and since then, they have had a significant impact on the field of deep learning. GANs are known for their ability to generate synthetic data that is incredibly realistic, ...