With the core concepts of autoencoders covered, this chapter focuses on the practical steps involved in constructing your first model designed for feature extraction. We will proceed through the entire workflow, from initial data preparation to extracting and understanding the learned features.

This chapter will guide you through:

• Preparing and preprocessing data, including normalization and scaling, for effective autoencoder training.
• Designing the encoder and decoder networks, considering aspects like the number of layers, neurons per layer, and choice of activation functions.
• Selecting an appropriate dimensionality for the latent space (the bottleneck layer).
• Choosing suitable loss functions, such as Mean Squared Error ($MSE$) or Binary Cross-Entropy ($BCE$), and configuring optimizers (e.g., Adam, SGD) with appropriate learning rates.
• Monitoring the training phase and then using the trained model to extract feature representations from the bottleneck.
• Methods for visualizing the latent space, particularly for lower-dimensional representations. A hands-on session will demonstrate building an autoencoder to extract features from a tabular dataset.