Deep Generative Modeling

Deep Generative Modeling

Explore deep generative modeling in this comprehensive lecture from MIT's Introduction to Deep Learning course. Delve into the importance of generative models, latent variable models, and autoencoders. Learn about variational autoencoders, including priors on latent distributions, the reparameterization trick, and applications in debiasing. Discover generative adversarial networks (GANs), their training process, and recent advances like conditional GANs and CycleGANs. Gain insights into the intuitions behind these powerful techniques and their practical applications. Conclude with a brief introduction to diffusion models, preparing you for the cutting edge of generative AI research.