Previous chapters introduced autoencoders and their core parts. Now, we examine how these networks actually learn from data. The central goal is to minimize reconstruction error, the difference between the original input and the autoencoder's output.

This chapter explains:

• Training Objective: How autoencoders aim to accurately reconstruct input.
• Loss Functions: The metrics used to measure reconstruction error, such as Mean Squared Error ($MSE$) and Binary Cross-Entropy ($BCE$).
• Optimization: The basic processes, like gradient descent, that adjust the autoencoder’s internal settings to improve performance.
• Data Flow: Understanding forward propagation (input to output) and a high-level view of backpropagation (error adjustment).
• Training Cycles: Concepts like epochs and batches that structure the learning process.

We will also briefly introduce overfitting and underfitting. By the end, you will understand the steps involved in preparing an autoencoder for training.