Adaptive Gradient Methods: Part 1

This lecture covers adaptive gradient methods, which adjust learning rates and update directions based on data to enhance convergence performance. The instructor describes various adaptive gradient methods, such as AdaGrad, ADAM, and RMSprop, and explains their impact on optimization scenarios. The lecture also delves into the motivation behind adaptive learning rates and the importance of scaling gradient vectors. Additionally, it explores the concepts of momentum acceleration and Nesterov accelerated gradient methods to improve convergence and reduce oscillations in nonconvex optimization problems.