Lectures related to Neural Network: Random Features and Kernel Regression

Related lectures (32)

Covers the concept of gradient descent for linear regression, explaining the iterative process of updating parameters.

Explores non-parametric regression techniques, including splines, bias-variance tradeoff, orthogonal functions, wavelets, and modulation estimators.