Artificial Neural Networks and Data Science
Abstract
Artificial neural networks (ANNs) consist of a family of techniques that are commonly employed to recognize and interpret patterns in big data that are used in prediction, clustering, classification, and identification of other previously unknown data patterns. This article describes foundational concepts that relate to ANNs, including an understanding of how ANNs are linked to biological concepts and the underlying ANN families. The article includes an explanation of common ANN methods, architecture/hyperparameter determination for initializing ANNs, and current research directions. The article concludes with a discussion on the need for algorithmic transparency and repeatability of research.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 14, 2022
- Source ID
- 10.4018/978-1-7998-9220-5.ch052
Entities
People
- Adam Moyer
- Steven Frimel
- Trevor Bihl
- William A. Young Ii
Organizations
- Air Force Research Laboratory
- Ohio University