Gradient Descent

This lecture covers the concept of gradient descent in the context of scalar cases, focusing on finding the minimum of a function by iteratively moving in the direction of the negative gradient. Various techniques and tricks are discussed to improve convergence speed and efficiency, such as adjusting the learning rate and using momentum. The instructor explains the mathematical formulation and practical applications of gradient descent, emphasizing its importance in optimization problems.