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Foundations of Neural Networks

In: Managing Artificial Intelligence

Author

Listed:
  • Nils Urbach

    (Frankfurt University of Applied Sciences)

  • Daniel Feulner

    (University of Bayreuth)

  • Tobias Guggenberger

    (University of Bayreuth)

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.

Suggested Citation

Handle: RePEc:spr:fuobcp:978-3-032-13308-3_3
DOI: 10.1007/978-3-032-13308-3_3
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