Stochastic gradient descent

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Related lectures (28)

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Structures in Non-Convex Optimization

Covers non-convex optimization, deep learning training problems, stochastic gradient descent, adaptive methods, and neural network architectures.

Convergence Analysis: Stochastic Gradient Algorithms

Explores the convergence analysis of stochastic gradient algorithms under various operational modes and step-size sequences.

Stochastic Optimization and Adaptive Gradient Methods

Explores stochastic optimization, adaptive gradient methods, recommender systems, and matrix factorization in user-item rating matrices.

Multilayer Neural Networks: Deep Learning

Covers the fundamentals of multilayer neural networks and deep learning.

Gradient Descent with Momentum

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

Neural Networks: Random Features and Kernel Regression

Explores random features in neural networks and kernel regression using stochastic gradient descent.

Optimization Methods in Machine Learning

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

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.