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Lecture
Mathematics of Data: Optimization Basics
Description
This lecture covers the basics of optimization, including definitions of metrics, vector norms, matrix norms, and convexity. It discusses continuity, differentiability, gradients, and convex functions. The lecture also explores logistic regression, strong convexity, convergence rates, and properties of strongly convex functions.
Official source
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