Gradient Descent: Optimization Techniques

This lecture covers the concepts of gradient descent, convex and non-convex loss functions, stochastic gradient descent, and early stopping in the context of neural networks training. It explains the importance of small weights at the beginning of gradient descent, the impact of validation loss increase, and the norm of parameters during training. The lecture also delves into the differences between standard and stochastic gradient descent, emphasizing the computational efficiency of the latter. Various optimization techniques and strategies are discussed, including the use of ADAMW optimizer and the concept of early stopping as a form of regularization.