Dimensionality Reduction: PCA & Autoencoders

This lecture covers the concepts of Principal Component Analysis (PCA) and Autoencoders for dimensionality reduction. Starting with the basics of PCA, it explains how to find the most important signal while removing noise. It then delves into Kernel PCA for nonlinear data. The discussion extends to Autoencoders, emphasizing their nonlinear mappings and applications in denoising and sparsity. The lecture also explores Convolutional Autoencoders and their role in generating new samples. Lastly, it showcases the practical use of Autoencoders for image retrieval and data generation.