Lecture
This lecture covers diffusion models, which are generative models that learn the distribution from which training samples are drawn based on training data. The instructor explains the concept of diffusion forward, starting with noisy data and gradually removing noise using a neural network. The lecture also delves into the process of generating samples from the same distribution, optimizing over it using various techniques like SGD, and the importance of denoising in the context of diffusion models.