Descent methods and line search: Inexact line search

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Optimization Basics: Unconstrained Optimization and Gradient Descent

Covers optimization basics, including unconstrained optimization and gradient descent methods for finding optimal solutions.

Newton's Method: Optimization & Indefiniteness

Covers Newton's Method for optimization and discusses the caveats of indefiniteness in optimization problems.

Recommender Systems: Matrix Factorization

Explores matrix factorization in recommender systems, covering optimization, evaluation metrics, and challenges in scaling.

Gradient Descent: Lipschitz Continuity

Explores Lipschitz continuity in gradient descent optimization and its implications on function optimization.

Gradient Descent

Covers the concept of gradient descent, a universal algorithm used to find the minimum of a function.

Optimization with Constraints: KKT Conditions

Covers the KKT conditions for optimization with constraints, essential for solving constrained optimization problems efficiently.

Gradient Descent: Optimization Techniques

Explores gradient descent, loss functions, and optimization techniques in neural network training.

Stochastic Gradient Descent: Non-convex Optimization Techniques

Discusses Stochastic Gradient Descent and its application in non-convex optimization, focusing on convergence rates and challenges in machine learning.

Proximal and Subgradient Descent: Optimization Techniques

Discusses proximal and subgradient descent methods for optimization in machine learning.

Deep Learning Building Blocks

Covers tensors, loss functions, autograd, and convolutional layers in deep learning.