Stochastic Gradient Descent

This lecture covers the concept of stochastic gradient descent, focusing on the optimization process with convex and p-Lipschitz functions. It explains the initialization, steps, and parameters involved in the algorithm, emphasizing the importance of choosing the step size. The lecture also delves into the proof of convergence and the application of the algorithm in practice, showcasing the iterative nature of the optimization process. Additionally, it explores the Mean-Field Method in neural networks, highlighting the role of neurons in hidden layers and input layers.