Neural network: Difference between revisions

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>.