Neural networks employ gradient descent to improve incrementally. The gradient refers to a set of directions derived (by computing the derivative of the loss function) that most effectively enhance predictions. By taking small steps in the direction of the gradient, recalculating the gradient, and repeating this process, a neural network can progressively optimize its performance during training.
