To effectively work with autoencoders for feature extraction, a solid grasp of underlying neural network mechanisms and dimensionality reduction techniques is necessary. This chapter revisits these foundational topics, ensuring you have the prerequisite knowledge before we build more complex models. We will connect these general concepts to how autoencoders learn and represent data.

Specifically, this chapter covers:

• A quick review of neural network components: layers, activation functions, loss functions, and optimizers.
• The difficulties associated with high-dimensional data, often termed the 'curse of dimensionality'.
• An introduction to different methods for dimensionality reduction.
• The distinction between linear (e.g., PCA) and non-linear dimensionality reduction.
• The function of feature extraction within machine learning pipelines.
• Steps for setting up a Python-based deep learning environment.
• A hands-on exercise implementing Principal Component Analysis (PCA).