Lecture
This lecture covers the fundamentals of autoencoders, starting with linear mappings in Principal Component Analysis (PCA) and extending to nonlinear mappings in autoencoders. It explores deep autoencoders, denoising autoencoders, and sparse autoencoders, highlighting their applications in dimensionality reduction and data retrieval. The concept of kernel PCA is introduced for nonlinear data, along with convolutional autoencoders for image data. The lecture concludes with a demonstration of autoencoder applications and mappings.