Foundations of Neural Networks

Nils Urbach (), Daniel Feulner () and Tobias Guggenberger ()
Additional contact information
Nils Urbach: Frankfurt University of Applied Sciences
Daniel Feulner: University of Bayreuth
Tobias Guggenberger: University of Bayreuth

Chapter 3 in Managing Artificial Intelligence, 2026, pp 51-70 from Springer

Abstract: Abstract This chapter examines the key components of neural networks, including input, hidden, and output layers, as well as the role of weights and activation functions in the learning process. Particular emphasis is placed on backpropagation as a core training technique. These concepts are explored in a structured manner, illustrated through the example of identifying handwritten digits, demonstrating how neural networks process and learn from data. The goal is to provide a clear and comprehensive introduction to the principles and mechanics of neural networks.

Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:fuobcp:978-3-032-13308-3_3

Ordering information: This item can be ordered from
http://www.springer.com/9783032133083

DOI: 10.1007/978-3-032-13308-3_3

Access Statistics for this chapter

More chapters in Future of Business and Finance from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().