Gradient Descent and Linear Regression

This lecture covers topics such as stochastic gradient descent, generalized linear regression, LASSO regularization, supervised and unsupervised learning, subgradient, and the role of the gradient in finding minima. It also discusses the iterative nature of gradient descent, different variants like ADAM, and the importance of hyperparameters.