AI Engineering Degree Practice Exam 2026 - Free AI Engineering Practice Questions and Study Guide

Gradient descent is an optimization algorithm used primarily in training machine learning models. Its primary goal is to minimize the cost function, which quantifies how well the model's predictions align with the actual outcomes. By applying the concept of derivatives, gradient descent calculates the gradient (or slope) of the cost function with respect to the model parameters. This gradient indicates the direction in which the cost function is steepest; therefore, by moving in the opposite direction (downwards on the slope), the algorithm updates the parameters to gradually reduce the overall cost.

This process involves taking small steps, controlled by a parameter known as the learning rate, to ensure that adjustments to the model parameters are made iteratively, leading to a more accurate representation of the training data over many iterations. As the algorithm progresses, it approaches a minimum point of the cost function, ideally where the model accurately predicts the outcomes.

In summary, gradient descent effectively leverages derivatives to minimize the cost function and fine-tunes the model parameters, improving the model's accuracy and performance in prediction tasks.