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:
1.1 Neural Network Components: A Quick Review
1.2 The Problem of High-Dimensional Data
1.3 Overview of Dimensionality Reduction Methods
1.4 Contrasting Linear and Non-linear Dimensionality Reduction
1.5 Feature Extraction's Role in ML Pipelines
1.6 Setting Up Your Deep Learning Environment
1.7 Hands-on: PCA for Dimensionality Reduction Practice
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