Lectures related to Generalization Error

Explores Banach spaces, emphasizing reflexivity and sequence convergence in a rigorous mathematical framework.

Explores the composition of applications in mathematics and the importance of understanding their properties.

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Explains how entropy measures uncertainty in a system based on possible outcomes.

Explores stable laws, limit theorems, and random variable properties.

Covers weak derivatives, their properties, and applications in functional analysis.

Introduces Cartesian product and induction for proofs using integers and sets.

Explores tail bounds, information bounds, and maximal leakage in the context of generalization error.