The use of descent gradients in AI Learning models

AI learning models use mathematical techniques to effectively minimize errors and improve prediction accuracy. Essential to these techniques is the use of the gradient descent, which outputs the correct way to adjust the model’s parameters to most effectively minimize error. The gradient descent is applied at the point of error produced by the program’s loss-function, a multi-variable function that mathematically represents the magnitude of error. This paper focuses on the implementation of the gradient descent, and its optimization of updating the models’ parameters. Multiple types of loss functions are examined, such as squared error and cross-entropy, and their influence on the gradient descent. By connecting the mathematical concept of the gradient descent and its real world meaning, this paper will enhance the understanding of the crucial process that drives model learning in modern Artificial Intelligence programs.