Gradient descent

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Related lectures (29)

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Newton's Method: Optimization & Indefiniteness

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

Multilayer Perceptron: Training and Optimization

Explores the multilayer perceptron model, training, optimization, data preprocessing, activation functions, backpropagation, and regularization.

Feed-forward Networks

Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.

Deep Learning: Convolutional Networks

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Descent methods and line search: Preconditioned steepest descent

Introduces preconditioning in optimization problems and explains steepest descent iteration.

Richardson Method: Preconditioned Iterative Solvers

Covers the Richardson method for solving linear systems with preconditioned iterative solvers and introduces the gradient method.

Algorithms for Composite Optimization

Explores algorithms for composite optimization, including proximal operators and gradient methods, with examples and theoretical bounds.

Momentum methods and nonlinear CG

Explores gradient descent with memory, momentum methods, conjugate gradients, and nonlinear CG on manifolds.

Proximal Gradient Descent: Optimization Techniques in Machine Learning

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