Recent Developments in Over-parametrized Neural Networks, Part I
Explore recent developments in over-parametrized neural networks in this comprehensive lecture from the Deep Learning Boot Camp. Delve into topics such as non-convex optimization, overparametrization, and theoretical problems in optimal optimization. Examine the taxonomy of results, including skip connections, expressivity, and geometric results. Investigate one-point convexity, Taylor's theorem, and the Royer algorithm. Learn about second-order stationary points, optimality conditions, and global optimality in neural networks. Discover strategies for second-order descent and stationary points in learning networks. Gain valuable insights from Jason Lee of the University of Southern California as he presents cutting-edge research and concepts in the field of deep learning and neural networks.