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1
Introduction
2
Deep Learning Puzzle
3
Supervised Learning
4
Formalization
5
Complexity
6
Empirical Risk
7
Constraint Forms
8
Interpolation
9
Fundamental Theorem
10
Question
11
Linear
12
Predicting
Description:
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.

Deep Learning I - Joan Bruna NYU

Paul G. Allen School
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