Having established the architectures and theoretical underpinnings of various autoencoders, from classic designs to variational and adversarial models, we now focus on their practical deployment and refinement. This chapter examines common application domains where autoencoders offer distinct advantages and addresses the techniques required to train these models effectively.

You will investigate how autoencoders are applied to tasks such as anomaly detection, utilizing reconstruction error as a key indicator. We will also cover their use in non-linear dimensionality reduction, data compression, and as a method for unsupervised pre-training in larger deep learning systems. Furthermore, applications like image denoising will be discussed.

Successfully applying autoencoders often requires careful training procedures. Therefore, this chapter also covers advanced optimization strategies suitable for these networks, methods for adjusting learning rates during training for better convergence, and systematic approaches to hyperparameter tuning. Mastering these techniques is frequently necessary for minimizing the chosen loss function, often denoted as $L$, and achieving reliable performance in practice. We will conclude with hands-on examples, such as building an autoencoder-based anomaly detection system.