Gradient descent serves as a fundamental method in machine learning. It enables models to refine their parameters by iteratively reducing the loss function. This approach involves determining the gradient of the error metric, which signals the direction of steepest ascent. By adjusting the parameters in the contrary direction of the gradient, the m