It is a first-order iterative algorithm for minimizing a differentiable multivariate function. The idea is to take repeated steps in the opposite direction of the gradient (or approximate gradient) of the …

What is gradient descent? Gradient descent is an optimization algorithm which is commonly-used to train machine learning models and neural networks. It trains machine learning models by …

Jul 11, 2025 · Neural networks are trained using Gradient Descent (or its variants) in combination with backpropagation. Backpropagation computes the gradients of the loss function with …

Oct 16, 2017 · In the last lesson we explored the structure of a neural network. Now, let’s talk about how the network learns by seeing many labeled training data. The core idea is a method …

Feb 28, 2025 · Almost all modern AI architectures, including GPT-4, ResNet and AlphaGo, rely on Gradient Descent to adjust their weights, improving prediction accuracy. Without Gradient …

Dec 26, 2025 · Every time a neural network learns to recognize a face, translate a sentence, or predict stock prices, gradient computation is working behind the scenes. This fundamental …

Nov 4, 2023 · In this post, I’m going to describe the algorithm of gradient descent, which is used to adjust the weights of an ANN. Let’s start with the basic concepts. Imagine we are at the top of …

Calculate gradients to determine how each model parameter contributed to model error. 4. Update each parameter using calculated gradients.

Apr 4, 2025 · Gradient descent can be applied to various machine learning algorithms, including linear regression, logistic regression, neural networks, and support vector machines. It …

Aug 5, 2025 · An in-depth explanation of Gradient Descent and how to avoid the problems of local minima and saddle points.