Lectures related to Matrix Factorization: Optimization and Evaluation

Covers the algorithm of gradient descent for optimization, including early stopping and regularization techniques.

Explores matrix inversion, conditions for invertibility, uniqueness of the inverse, and elementary matrices for inversion.

Explores gradient descent, loss functions, and optimization techniques in neural network training.

Explores optimal control and KKT conditions for non-linear optimization with constraints.