Deep Generative Models: Part 2

This lecture covers deep generative models, including mixtures of multinomials and LDA for generating new documents, PCA for dimensionality reduction, deep autoencoders with nonlinear activation functions, convolutional autoencoders for image generation, and the training process using stochastic gradient descent. It also discusses using autoencoders as generative models, the need for defining distributions over latent variables, and the visual interpretation of VAE. The lecture concludes with a recap of GANs, conditional GANs, and the limitations of Bag of Words models.