Explores score-based generative modelling through stochastic differential equations, emphasizing denoising score matching and diffusion probabilistic models.
Delves into score-based generative models, exploring learning natural distributions and the impact of neural network architecture on robustness.
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Delves into the impact of deep learning on non-conceptual knowledge systems and the advancements in transformers and generative adversarial networks.
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
