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==Introduction== | ==Introduction== | ||
[[File:Ann.png|thumb|Figure 1. Input, hidden, and output layers of an ANN. Source: Nahar (2012).]] | [[File:Ann.png|thumb|Figure 1. Input, hidden, and output layers of an ANN. Source: Nahar (2012).]] | ||
An artificial neural network (ANN), or just neural network (NN) for simplicity, is a massively parallel distributed processor made up of simple, interconnected processing units. It is an information processing paradigm – a computing system - inspired by biological nervous systems (e.g. the brain) and how they process information, where a large number of highly interconnected processing units work in unison to solve specific problems. The scale of an artificial neural network is smaller when compared to their biological counterpart. For example, a large ANN might have hundreds or thousands of processor units while a biological nervous system (e.g. a mammalian brain) has billions of neurons <ref name="”1”">Zaytsev, O. (2016). A Concise Introduction to Machine Learning with Artificial Neural Networks. Retrieved from http://www.academia.edu/25708860/A_Concise_Introduction_to_Machine_Learning_with_Artificial_Neural_Networks</ref> <ref name="”2”">Nahar, K. (2012). Artificial Neural Network. COMPUSOFT, 1(2): 25-27</ref> <ref name="”3”">A basic introduction to neural networks. Retrieved from http://pages.cs.wisc.edu/~bolo/shipyard/neural/local.html</ref>. Neural networks - a set of algorithms designed to recognize patterns - interpret data, labeling or clustering raw input. They recognize numerical patterns contained in vectors, into which all real-world data, such as images, sound, text, or time series need to be translated <ref name="”4”">Deeplearning4j. Introduction to deep neural networks. Retrieved from https://deeplearning4j.org/neuralnet-overview.html#introduction-to-deep-neural-networks</ref>. | [[Neural network]]s are [[machine learning]] [[algorithm]]s [[model]]ed after the structure and function of the human brain, designed to recognize patterns and make decisions based on [[input data]]. An artificial neural network (ANN), or just neural network (NN) for simplicity, is a massively parallel distributed processor made up of simple, interconnected processing units. It is an information processing paradigm – a computing system - inspired by biological nervous systems (e.g. the brain) and how they process information, where a large number of highly interconnected processing units work in unison to solve specific problems. The scale of an artificial neural network is smaller when compared to their biological counterpart. For example, a large ANN might have hundreds or thousands of processor units while a biological nervous system (e.g. a mammalian brain) has billions of neurons <ref name="”1”">Zaytsev, O. (2016). A Concise Introduction to Machine Learning with Artificial Neural Networks. Retrieved from http://www.academia.edu/25708860/A_Concise_Introduction_to_Machine_Learning_with_Artificial_Neural_Networks</ref> <ref name="”2”">Nahar, K. (2012). Artificial Neural Network. COMPUSOFT, 1(2): 25-27</ref> <ref name="”3”">A basic introduction to neural networks. Retrieved from http://pages.cs.wisc.edu/~bolo/shipyard/neural/local.html</ref>. Neural networks - a set of algorithms designed to recognize patterns - interpret data, labeling or clustering raw input. They recognize numerical patterns contained in vectors, into which all real-world data, such as images, sound, text, or time series need to be translated <ref name="”4”">Deeplearning4j. Introduction to deep neural networks. Retrieved from https://deeplearning4j.org/neuralnet-overview.html#introduction-to-deep-neural-networks</ref>. | ||
The simple processing units of an ANN are, in loose terms, the artificial equivalent of their biological counterpart, the neurons. Biological neurons receive signals through synapses. When the signals are strong enough and surpass a certain threshold, the neuron is activated, emitting a signal through the axon that might be directed to another synapse <ref name="”4”" /> <ref name="”5”">Kriesel, D. (2007). A Brief Introduction to Neural Networks. Retrieved from http://www.dkriesel.com</ref> <ref name="”6”">Gershenson, C. (2003). Artificial neural networks for beginners. arXiv:cs/0308031v1 [cs.NE]</ref>. According to Gershenson (2003), the nodes (artificial neurons) “consist of inputs (like synapses), which are multiplied by weights (strength of the respective signals), and then computed by a mathematical function which determines the activation of the neuron. Another function (which may be the identity) computes the output of the artificial neuron (sometimes in dependence of a certain threshold). ANNs combine artificial neurons in order to process information <ref name="”6”" />.” | The simple processing units of an ANN are, in loose terms, the artificial equivalent of their biological counterpart, the neurons. Biological neurons receive signals through synapses. When the signals are strong enough and surpass a certain threshold, the neuron is activated, emitting a signal through the axon that might be directed to another synapse <ref name="”4”" /> <ref name="”5”">Kriesel, D. (2007). A Brief Introduction to Neural Networks. Retrieved from http://www.dkriesel.com</ref> <ref name="”6”">Gershenson, C. (2003). Artificial neural networks for beginners. arXiv:cs/0308031v1 [cs.NE]</ref>. According to Gershenson (2003), the nodes (artificial neurons) “consist of inputs (like synapses), which are multiplied by weights (strength of the respective signals), and then computed by a mathematical function which determines the activation of the neuron. Another function (which may be the identity) computes the output of the artificial neuron (sometimes in dependence of a certain threshold). ANNs combine artificial neurons in order to process information <ref name="”6”" />.” | ||