6.1 Introduction to Deep Learning and GANs
This section provides an overview of deep learning and GANs, including their applications and importance in the field of artificial intelligence. It also introduces the concept of generative models and their role in GANs.
6.2 Architecture of GANs
This chapter covers the architecture of GANs, including the generator and discriminator networks. It explains how these networks work together to generate new data samples.
6.3 Training GANs
In this section, you will learn about the training techniques used in GANs, such as backpropagation and gradient descent. It also covers common challenges in training GANs and how to overcome them.
6.4 Applications of GANs
This chapter explores the various applications of GANs, such as image synthesis, data augmentation, and style transfer. It also discusses the ethical considerations of using GANs in certain applications.
6.5 Evaluating GANs
This section covers the evaluation metrics used to assess the performance of GANs, such as inception score and Frechet Inception Distance (FID). It also discusses the limitations of these metrics and alternative evaluation methods.
6.6 Future of GANs
This chapter explores the potential future developments of GANs, such as improving their stability and scalability. It also discusses the potential impact of GANs on various industries, such as healthcare and entertainment.