Simon Du, an Assistant Professor at the University of Washington, delves into the theoretical foundations of deep learning and gradient descent. He discusses the intricacies of addressing non-convex problems, revealing challenges and insights from his research. The conversation highlights the significance of the neural tangent kernel and its implications for optimization and generalization. Simon also shares practical tips for reading research papers, drawing connections between theory and practice, and navigating a successful research career.
