Gradient descent is an optimization algorithm used to minimize the loss function in neural networks by adjusting the model's parameters (weights) in the direction of the steepest decrease in error. In each iteration, the model computes the gradient (derivative) of the loss with respect to the parameters and updates the weights accordingly.
There are different variants of gradient descent, including batch gradient descent, stochastic gradient descent (SGD), and mini-batch gradient descent, each differing in the amount of data used to compute the gradient in each iteration.
Gradient descent is the backbone of training neural networks and other machine learning models, enabling them to learn from data by iteratively reducing the prediction error.