Deep Generative Models

This lecture introduces deep generative models, starting with a recap on mixture of multinoullis and latent Dirichlet allocation. It then delves into variational autoencoders and generative adversarial networks, discussing their architectures, training procedures, and weaknesses. The lecture concludes with a look at deep convolutional GANs and practical applications of autoencoders as generative models.