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Advances in Computer and Communication

DOI:http://dx.doi.org/10.26855/acc.2021.08.001

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In Shortly about Neural Networks

Siniša Franjić1,*, Dario Galić2

1Independent Researcher, Osijek, Croatia.

2Faculty of Dental Medicine and Health, Osijek, Croatia.

*Corresponding author: Siniša Franjić

Date: August 3,2021 Hits: 579

Abstract

A neural network is a collection of neurons that are interconnected and interactive through signal processing operations. The traditional term “neural network” refers to a biological neural network, i.e., a network of biological neurons. The modern meaning of this term also includes artificial neural networks, built of artificial neurons or nodes. Machine learning includes adaptive mechanisms that allow computers to learn from experience, learn by example and by analogy. Learning opportunities can improve the performance of an intelligent system over time. One of the most popular approaches to machine learning is artificial neural networks. An artificial neural network consists of several very simple and interconnected processors, called neurons, which are based on modeling biological neurons in the brain. Neurons are connected by calculated connections that pass signals from one neuron to another. Each connection has a numerical weight associated with it. Weights are the basis of long-term memory in artificial neural networks. They express strength or importance for each neuron input. An artificial neural network “learns” through repeated adjustments of these weights.

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In Shortly about Neural Networks

How to cite this paper: Siniša Franjić, Dario Galić. (2021) In Shortly about Neural Networks. Advances in Computer and Communication2(1), 15-19.

DOI: http://dx.doi.org/10.26855/acc.2021.08.001