Neural networks under SGD

This lecture covers the optimization of neural networks using Stochastic Gradient Descent (SGD). The instructor explains the concept of dual risk versus empirical risk, the evolution of sparsity, and the speed and direction of the gradient flow. The lecture delves into the relationship between the gradient and speed, the discretization of equations, and the divergence in the context of neural networks.