Neural Networks and Deep Learning

Zeynep Burcu Kizilkan, Mahmut Sami Sivri, Ibrahim Yazici, Omer Faruk Beyca*

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümBölümbilirkişi

5 Atıf (Scopus)

Özet

Artificial neural network is a well-known machine learning technique inspired by biological neural network structures. It mimics the human brain’s working mechanism by artificially forming a neural network. In this book, artificial neural networks are referred to as neural networks. The principal idea of a neural network is to show transformation between input and output as connections between neurons in a sequence (arrangement) of layers (White L, Togneri R, Liu W, Bennamoun M (2019) Neural Representations of Natural Language, vol. 783. Singapore: Springer Singapore). Neural networks are mostly used for prediction, decision making, pattern recognition, and novelty detection (Si in Data Mining Techniques for the Life Sciences, Humana Press, Totowa, NJ, 2010). The first is that without domain expertise, neural networks may assist in estimating function structures and parameters (Si in Data Mining Techniques for the Life Sciences, Humana Press, Totowa, NJ, 2010).

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıSpringer Series in Advanced Manufacturing
YayınlayanSpringer Nature
Sayfalar127-151
Sayfa sayısı25
DOI'lar
Yayın durumuYayınlandı - 2022

Yayın serisi

AdıSpringer Series in Advanced Manufacturing
ISSN (Basılı)1860-5168
ISSN (Elektronik)2196-1735

Bibliyografik not

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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