Introduction to Iterative Optimization
[Academic Review] This presentation makes an introduction to first and second order optimization along their python implementation.
This is the part one of the optimization presentations; Topics include: Determining Covexity (1), Likelihood, log-Likelihood, Gradient, Hessian, Gradient Descent (GD), Accelerated and Stochastic GD, and Newton’s Method (2), Loss Function (3), Alternating Direction Method of Multipliers (ADMM) (4), Coordinate Descent (5), and Proximal GD (6).
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