Learn key optimization techniques in deep learning through a comprehensive lecture covering stochastic gradient descent, mini-batch processing, and momentum-based methods. Explore Stein's unbiased risk estimator and its applications in optimization algorithms. Gain practical insights into how these fundamental concepts work together to improve neural network training and performance in modern deep learning applications.