From Stochastic Gradient Descent to Non-Smooth Optimization

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Related lectures (28)

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Stochastic Gradient Descent: Optimization Techniques

Explores Stochastic Gradient Descent with Averaging, comparing it with Gradient Descent, and discusses challenges in non-convex optimization and sparse recovery techniques.

Feature Engineering: Polynomial Regression

Covers fitting linear regression on features of the original predictors for flexible feature representation.

Linear Regression: Basics and Estimation

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Optimization Techniques: Gradient Method Overview

Discusses the gradient method for optimization, focusing on its application in machine learning and the conditions for convergence.

Linear Models: Continued

Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.

Cross-Validation: Techniques and Applications

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

Back to Linear Regression

Covers linear regression, regularization, inverse problems, X-ray tomography, image reconstruction, data inference, and detector intensity.

Gradient Descent: Optimization Techniques

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