Lectures related to Dimensional regularization

Covers the computation of loops in quantum field theory, focusing on dimensional regularization and Euclidean space.

Covers the concept of gradient descent, a universal algorithm used to find the minimum of a function.

Covers linear regression, regularization, and probabilistic models in generating labels.

Explores neural network regularization, optimization, and practical implementation tips.

Covers linear classification using logistic regression, regularization, and multiclass classification.