Having established the architecture of autoencoders and the mechanics of their training, we now shift our attention to a primary application: how they learn from data. The process of compressing input into a smaller, dense form within the bottleneck, and then reconstructing it, compels the network to discern and retain the most salient characteristics of the input.

This chapter examines this capability. We will look into how autoencoders function as tools for feature learning, automatically identifying and extracting useful attributes from raw data without explicit programming for these specific features. You will understand how the data within the bottleneck layer can be interpreted as a set of learned features. We will also cover how this directly enables dimensionality reduction, a technique for simplifying datasets by reducing the number of variables while aiming to preserve essential information. The discussion will include a comparison between such automatically learned features and those derived from traditional manual feature engineering, along with approaches to inspect or visualize the features an autoencoder has learned.