Gradient Descent

This lecture covers the algorithm of gradient descent, aiming to minimize a function by iteratively moving in the direction of the steepest decrease. It explains the process of slowly approaching the minimum, avoiding overshooting. The lecture also delves into the concept of Taylor series and the search for the minimum point. Topics include the impact of step size on convergence, the importance of regularization, and the implications of different learning rates. The content progresses through various mathematical expressions and iterative steps to find the optimal solution.