Optimizing Loss Functions: Gradient Descent Variants

This lecture covers the concept of early stopping in gradient descent, emphasizing the importance of minimizing test error over loss function optimization. The instructor explains the use of subgradients for non-differentiable functions and introduces stochastic gradient descent as a faster alternative. Variants like momentum-based optimization and adaptive learning rates are discussed, highlighting the trade-offs between speed and convergence to global minima.