Having established how to construct and train various autoencoder architectures, this chapter centers on the practical application of the features they generate. We will cover selecting an appropriate autoencoder model for your specific problem and data. You'll learn techniques for tuning hyperparameters to optimize performance and methods for evaluating the quality of the extracted features. We will then proceed to integrate these features into supervised learning models, examine applications like anomaly detection and data compression, and consider transfer learning approaches. Finally, we'll address common implementation issues and work through an example of using autoencoder features in a classification task.