Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.
Explores Variational Auto-Encoders, Bayesian inference, attention-based latent spaces, and the effectiveness of Transformers in language processing.
Introduces topic models, covering clustering, GMM, LDA, Dirichlet distribution, and variational inference.
Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.
Covers non-linear dimensionality reduction techniques using autoencoders, deep autoencoders, and convolutional autoencoders for various applications.
Explores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.
