Deep Learning: Overparameterization and Generalization

This lecture explores the concepts of overparameterization and generalization in deep learning models, discussing the impact of model size, training samples, and test errors. It delves into the phenomenon of overfitting and underfitting, showcasing the importance of finding the 'sweet spot' in model capacity. The lecture also covers the implications of double descent risk curves and the existence of bad global minima in training deep neural networks. Additionally, it examines the role of implicit regularization in stochastic gradient descent and the ability of neural networks to fit random labels. The presentation concludes with insights on self-supervised learning, representation learning, and generative models.