Term of Award
Summer 2021
Degree Name
Master of Science in Mathematics (M.S.)
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Department of Mathematical Sciences
Committee Chair
Yan Wu
Committee Member 1
Arpita Chatterjee
Committee Member 2
Yongki Lee
Abstract
Deep learning has a substantial amount of real-life applications, making it an increasingly popular subset of artificial intelligence over the last decade. These applications come to fruition due to the tireless research and implementation of neural networks. This paper goes into detail on the implementation of supervised learning neural networks utilizing MATLAB, with the purpose being to generate a neural network based on specifications given by a user. Such specifications involve how many layers are in the network, and how many nodes are in each layer. The neural network is then trained based on known sample values of a function to reveal some intrinsic properties of said function.
OCLC Number
1422811586
Catalog Permalink
https://galileo-georgiasouthern.primo.exlibrisgroup.com/permalink/01GALI_GASOUTH/1r4bu70/alma9916562044002950
Recommended Citation
Phillips, Kane A., "Implementing a Neural Network for Supervised Learning with a Random Configuration of Layers and Nodes" (2021). Electronic Theses and Dissertations. 2304.
https://digitalcommons.georgiasouthern.edu/etd/2304
Research Data and Supplementary Material
No
