This chapter establishes the essential groundwork for our study of Variational Autoencoders. We begin by examining probabilistic generative models from an advanced standpoint, understanding how they aim to capture the underlying distributions of data. We will also cover the core principles of representation learning, focusing on how data can be transformed into more useful forms for downstream tasks. These foundational topics are key to grasping the architecture and operation of sophisticated VAEs.

Our study will start with an advanced perspective on probabilistic models and then proceed to the theory and formulation of latent variable models. You will learn the core principles of representation learning and methods for evaluating representation quality, including established metrics. A review of standard autoencoders will clarify their limitations for generative tasks, underscoring the need for models like VAEs. We will also consider the role of information theory within representation learning. Completing this chapter will provide you with the necessary background to effectively engage with the subsequent material on VAEs.