SGD and Mean Field Analysis

This lecture covers the concepts of Stochastic Gradient Descent (SGD) and Mean Field Analysis in the context of two-layer neural networks. The instructor explains the iterative process of SGD and its application in analyzing neural networks. The lecture delves into the mathematical foundations of mean field analysis and its significance in understanding the behavior of neural networks. Various theorems related to the convergence of SGD and the representation power of two-layer neural networks are discussed. The presentation concludes with a detailed explanation of stochastic gradient computation and its role in optimizing neural network models.