Lectures related to Stochastic Gradient Descent: Optimization Techniques

Explores the optimality of convergence rate in gradient descent and acceleration techniques for convex and non-convex problems.

Provides an overview of optimization techniques, focusing on gradient descent and properties of convex functions in machine learning.

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

Introduces convex optimization, focusing on the importance of convexity in algorithms and optimization problems.

Explores stochastic gradient descent optimization and the Mean-Field Method in neural networks.