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Artificial Neural Networks

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Shallow and Deep Learning Principles

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Abstract

This chapter presents a comprehensive explanation of early artificial neural network (ANN) structural and procedural functionality in nonlinear problem solving as preliminary shallow learning aspects across artificial intelligence (AI) methodologies. ANNs are like the human brain in the form of parallel processing modeling inspired by the brain working system. They can process information quickly because they work in parallel and their hardware is easier to implement than other methods. One of the most important features of ANN, which includes many features beyond the models made with regression and stochastic methods, is that it does not require some assumptions about the event or data at the beginning. The connection of the ANN to biological system is explained with characteristic definitions. Various ANN modeling application disciplines and some practical applications are given. The principles of the basic ANN alternative perceptron are explained with numerical applications. The educational contents of ANN are explained to improve the philosophical and logical thinking principles of science. For this purpose, unsupervised and supervised alternatives of ANN procedures are shown for classification and regression purposes.

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Authors and Affiliations

  1. Istanbul Medipol University, Istanbul, Türkiye

    Zekâi Şen

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  1. Zekâi Şen
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Şen, Z. (2023). Artificial Neural Networks. In: Shallow and Deep Learning Principles. Springer, Cham. https://doi.org/10.1007/978-3-031-29555-3_7

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  • DOI: https://doi.org/10.1007/978-3-031-29555-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-29554-6

  • Online ISBN: 978-3-031-29555-3

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