Gradient Descent

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Related lectures (27)

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Gradient Descent with Momentum

Explores the use of momentum in gradient descent to enhance speed and stability.

Optimization Techniques: Gradient Method Overview

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

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Optimization Methods in Machine Learning

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

Linear Systems: Convergence and Methods

Explores linear systems, convergence, and solving methods with a focus on CPU time and memory requirements.

Gradient Descent: Principles and Applications

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

Optimization: Gradient Descent and Subgradients

Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.

Proximal Gradient Descent: Optimization Techniques in Machine Learning

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

Newton's Method: Optimization Techniques

Explores optimization techniques like gradient descent, line search, and Newton's method for efficient problem-solving.

Coordinate Descent: Efficient Optimization Techniques

Covers coordinate descent, a method for optimizing functions by updating one coordinate at a time.