Understanding AutoencodersExplores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.
Dimensionality Reduction: PCA & LDACovers PCA and LDA for dimensionality reduction, explaining variance maximization, eigenvector problems, and the benefits of Kernel PCA for nonlinear data.
Deep Generative ModelsCovers deep generative models, including variational autoencoders, GANs, and deep convolutional GANs.
Deep Generative Models: Part 2Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.
Non-Linear Dimensionality ReductionCovers non-linear dimensionality reduction techniques using autoencoders, deep autoencoders, and convolutional autoencoders for various applications.
Data Representation: PCACovers data representation using PCA for dimensionality reduction, focusing on signal preservation and noise removal.
Machine Learning FundamentalsIntroduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Dimensionality ReductionIntroduces artificial neural networks and explores various dimensionality reduction techniques like PCA, LDA, Kernel PCA, and t-SNE.