Deep Learning I - Joan Bruna NYU

Explore the foundations of deep learning in this comprehensive lecture by Joan Bruna from NYU. Delve into key concepts including supervised learning, formalization, complexity, empirical risk, and constraint forms. Examine the fundamental theorem of machine learning and its implications for linear prediction and interpolation. Gain valuable insights into the deep learning puzzle and its practical applications in modern artificial intelligence.

Introduction
Deep Learning Puzzle
Supervised Learning
Formalization
Complexity
Empirical Risk
Constraint Forms
Interpolation
Fundamental Theorem
Question
Linear
Predicting