What is the main function of the gradient descent algorithm?

The main function of the gradient descent algorithm is to minimize the loss function. In machine learning, the loss function quantifies how well a model's predictions match the actual data. By minimizing the loss function, gradient descent helps in optimizing the parameters of the model so that it can make more accurate predictions.

The algorithm works by iteratively adjusting the parameters in the direction that reduces the loss. This is accomplished by computing the gradient (the derivative) of the loss function with respect to the model parameters and moving them in the opposite direction of the gradient. This process continues until the algorithm converges to a minimum point of the loss function, ideally leading to a model that generalizes well to unseen data.

The focus on minimizing the loss function is what distinguishes gradient descent as a key optimization technique in training machine learning models, as the goal is always to improve model performance by reducing prediction errors.