Gradient Descent Methods: Theory and Computation

Related lectures (27)

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Covers gradient descent, its principles, applications, and convergence rates in optimization for machine learning.

Discusses proximal gradient descent and its applications in optimizing machine learning algorithms.

Explores the role of computation in optimization, focusing on gradient descent for convex and nonconvex problems.

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

Covers non-convex optimization techniques and their applications in machine learning.

Explores the role of computation in data mathematics, focusing on iterative methods, optimization, estimators, and descent principles.

Covers convex optimization problems and gradient-based algorithms to find the global minimum.

Introduces proximal operators, gradient methods, and constrained optimization, exploring their convergence and practical applications.

Explores stochastic gradient descent, covering convergence rates, acceleration, and practical applications in optimization problems.

Explores optimization methods in machine learning, emphasizing gradients, costs, and computational efforts for efficient model training.