Learning Representations via Autoencoders and Generative Models

This chapter focuses on autoencoders and generative models for learning representations. It covers the basic architecture of autoencoders, various types of autoencoders like denoising, sparse, and variational autoencoders, and advanced architectures such as convolutional and recurrent autoencoders. The chapter introduces generative models, including GANs, and their applications in data generation and augmentation. Practical applications in style transfer and data augmentation are provided, with exercises to reinforce the concepts.