group
What campus are you from?
Daytona Beach
Authors' Class Standing
Alexander Wheeler, Sophomore Pietro Furlan, Sophomore Nathaniel Skarupa, Sophomore James Wilburn, Sophomore Nicholas Alberts, Sophomore Mason Cisco, Sophomore"
Lead Presenter's Name
Alexander Wheeler
Faculty Mentor Name
Dr. Hemanta Kunwar
Abstract
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.
Did this research project receive funding support from the Office of Undergraduate Research.
No
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.