BYU

Abstract by Spencer Reschke

Personal Infomation


Presenter's Name

Spencer Reschke

Co-Presenters

None

Degree Level

Undergraduate

Co-Authors

None

Abstract Infomation


Department

Mathematics

Faculty Advisor

Mark Hughes

Title

An Introduction to Artificial Neural Networks

Abstract

Artificial Neural Networks (ANNs) are among the most widely used and most successful algorithms used today in the field of machine learning. Because of their ability to  model nonlinear functions, neural networks have been successfully implemented to solve problems in a variety of fields including medicine, face/image recognition, and computer vision. Due to the stunning complexity of ANNs, for many people it can be intimidating to begin learning about them. In this talk, I’ll introduce the basics of ANNs-forward propogation, activation functions, cost functions, and back propogation-all in a supervised learning setting. As a potential application of ANNs, I’ll then discuss how ANNs, Gershgorwin’s Circle Theorem, and isospectral reductions could be used to improve estimates of matrix spectra.