Learning and Generalization in Over-parametrized Neural Networks - Going Beyond Kernels

Learning and Generalization in Over-parametrized Neural Networks - Going Beyond Kernels

Explore the frontiers of deep learning in this 50-minute lecture by Yuanzhi Li from Stanford University. Delve into the complexities of learning and generalization in over-parametrized neural networks, going beyond traditional kernel methods. Examine key concepts such as tangent kernels, learning algorithms, and generalization theorems. Gain insights into why neural networks are effective and understand the intuitions behind their complexity. Discover the potential future directions of this field and learn how to feed better neural networks. This talk, part of the Frontiers of Deep Learning series at the Simons Institute, offers a comprehensive overview of cutting-edge research in neural network theory and practice.