Optimization: Gradient Descent

This lecture covers the concept of optimization in machine learning, focusing on the Gradient Descent algorithm. It explains how to minimize a cost function by iteratively updating the parameters in the opposite direction of the gradient. The lecture discusses the challenges of grid search for large parameter spaces and introduces the concept of gradient descent for finding the optimal parameters. It also delves into the theoretical motivation behind the algorithm, the use of stochastic gradient descent, and the role of subgradients in non-smooth optimization.