Deep Generative Models: Part 2Explores deep generative models, including mixtures of multinomials, PCA, deep autoencoders, convolutional autoencoders, and GANs.
Machine Learning FundamentalsIntroduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Machine Learning ReviewCovers 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.
Understanding AutoencodersExplores autoencoders, from linear mappings in PCA to nonlinear mappings, deep autoencoders, and their applications.
Non-Linear Dimensionality ReductionCovers non-linear dimensionality reduction techniques using autoencoders, deep autoencoders, and convolutional autoencoders for various applications.
Deep Learning Modus OperandiExplores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.