10 Deep Learning Theory
10.1 Approximation Theory
10.1.1 Universal Approximation
10.1.2 Kernel Methods and Random Feature Models
10.1.3 Benefits of Depth
10.2 Optimization Theory
10.2.1 Neural Tangent Kernel
10.2.2 Margin Maximization and Implicit Bias
10.2.3 Edge of Stability
10.3 Classic Models for Learning Theory
10.3.1 Linear Models
10.3.2 Statistical Query
10.1.1 Universal Approximation
10.1.2 Kernel Methods and Random Feature Models
10.1.3 Benefits of Depth
10.2 Optimization Theory
10.2.1 Neural Tangent Kernel
10.2.2 Margin Maximization and Implicit Bias
10.2.3 Edge of Stability
10.3 Classic Models for Learning Theory
10.3.1 Linear Models
10.3.2 Statistical Query