Gradient descent is a method for unconstrained mathematical optimization. It is a first-order iterative algorithm for minimizing a differentiable multivariate function.

The idea of gradient descent is then to move in the direction that minimizes the approximation of the objective above, that is, move a certain amount > 0 in the direction −∇ ( ) of steepest descent of the …

Jan 5, 2026 · Gradient Descent is an optimisation algorithm used to minimize a model’s error by iteratively adjusting its parameters. By moving step‑by‑step in the direction of the steepest decrease …

Feb 28, 2025 · Gradient Descent is the core optimization algorithm for machine learning and deep learning models. Almost all modern AI architectures, including GPT-4, ResNet and AlphaGo, rely on …

Apr 4, 2025 · In this article, you will learn about gradient descent in machine learning, understand how gradient descent works, and explore the gradient descent algorithm’s applications.

Gradient descent is an optimization algorithm which is commonly-used to train machine learning models and neural networks. It trains machine learning models by minimizing errors between predicted and …

Gradient descent is iterative. First, we obtain an initial estimate w1 of the unknown parameter vector. How we obtain this vector depends on the problem; one approach is to randomly-sample values for …

Jan 22, 2025 · In the ever-evolving landscape of artificial intelligence and machine learning, Gradient Descent stands out as one of the most pivotal optimization algorithms. From training linear …

Jan 12, 2026 · Learn how gradient descent iteratively finds the weight and bias that minimize a model's loss. This page explains how the gradient descent algorithm works, and how to determine that a …

There is an enormous and fascinating literature on the mathematical and algorithmic foundations of optimization, but for this class we will consider one of the simplest methods, called gradient descent.