Introduces optimization basics, covering norms, convexity, differentiability, and more, with a focus on metrics, vector norms, matrix norms, and continuity.
Covers optimization basics, including metrics, norms, convexity, gradients, and logistic regression, with a focus on strong convexity and convergence rates.
Covers vectorization in Python using Numpy for efficient scientific computing, emphasizing the benefits of avoiding for loops and demonstrating practical applications.
