Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.
Illustrates the efficiency of logarithmic search algorithms over linear ones, emphasizing the importance of data modeling and the trade-off between sorting and searching costs.
