This lecture delves into variance reduction techniques in deep learning, starting with an exploration of gradient descent versus stochastic gradient descent. The instructor explains how to decrease variance while using a constant step-size and introduces the concept of mini-batch stochastic gradient descent. The lecture covers the Stochastic Variance Reduced Gradient (SVRG) method, convergence analysis, and the performance comparison of different algorithms. It concludes with a discussion on variance reduction for non-convex problems and provides insights into the application of variance reduction techniques in deep learning.