What is the purpose of gradient descent in machine learning?

The purpose of gradient descent in machine learning is to serve as an optimization algorithm that minimizes the loss function by iteratively adjusting the model parameters. In machine learning, the loss function measures how well the model's predictions match the actual outcomes; thus, minimizing this function is crucial for improving the model's performance.

Gradient descent works by calculating the gradient (or the derivative) of the loss function with respect to the model parameters. This gradient indicates the direction of steepest ascent, so by taking steps in the opposite direction, we effectively move towards the minimum of the loss function. The size of these steps is controlled by a parameter known as the learning rate. By repeatedly updating the parameters based on the gradients, the algorithm converges toward an optimal set of model parameters that minimize the loss.

This fundamental concept of gradient descent underpins many machine learning algorithms, such as linear regression, logistic regression, and neural networks, where finding the best parameters is essential for model training and performance.