Explore the groundbreaking theory of large-scale learning with Deep Neural Networks in this Stanford University seminar. Delve into the correspondence between Deep Learning and the Information Bottleneck framework as presented by Dr. Naftali Tishby, a professor of Computer Science at the Hebrew University of Jerusalem. Discover a new generalization bound, the input-compression bound, and its importance for good generalization. Learn how mutual information on input and output variables in the last hidden layer characterizes sample complexity and accuracy in large-scale Deep Neural Networks. Understand how Stochastic Gradient Descent achieves optimal bounds in Deep Learning, providing insights into the benefits of hidden layers and design principles. Gain a comprehensive understanding of the interface between computer science, statistical physics, and computational neuroscience, including applications of statistical physics and information theory in computational learning theory.