Explore the dynamics of gradient descent in deep learning through a comprehensive lecture that delves into optimization and generalization. Analyze the interplay between classical machine learning and deep learning approaches, focusing on gradient flow and end-to-end dynamics. Investigate implicit preconditioning, deep matrix factorization, and the dynamics of singular values in matrix completion problems. Gain insights into the role of nonlinearity in deep learning optimization and generalization, presented by Nadav Cohen from Tel-Aviv University as part of the "Learning and Testing in High Dimensions" series at the Simons Institute.