Autoencoders are a class of artificial neural networks primarily used for unsupervised learning. Their main function is to learn compressed representations of input data, often termed 'codings', and then to reconstruct the original input from these codings as accurately as possible. This process allows them to discover underlying structures in data.

This chapter lays the foundation for your understanding of autoencoders. We will start by positioning autoencoders within the context of unsupervised learning and briefly touching upon the basics of neural networks, which are their building blocks. You will learn about the central idea behind autoencoders: learning to reconstruct data. We'll then break down their architecture into the encoder, the bottleneck (or latent space), and the decoder. We will also discuss why learning meaningful data representations is important, illustrate the core mechanism with a simple analogy, and identify the types of problems autoencoders initially address. By the end of this chapter, you will have a clear grasp of what autoencoders are and why they are a valuable tool in machine learning.