What is Neural Networks?

Neural networks mirrors the behavior of the human central nervous system, passing to computer applications learning patterns and solve problems in the fields of Artificial Intelligence such as machine learning process.

A Neural Network or Simulated Neural Network, is a linked set of natural or artificial neurons that use a mathematical model allowing information being computed. ANN (artificial neuron network) is usually an adaptive system that changes its structure on external or internal information basis that flows through the network.

An artificial neural network connects a simple processing elements (neurons) which can illustrate global behavior, controlled by the ties between the processing components and component parameters. Artificial neurons were first introduced in 1943 by Warren McCulloch, a neurophysiologist, and Walter Pitts, a logician, who initially cooperated at the University of Chicago.

One classical type of artificial neural network is the recurrent Hopfield network.

First concept of neural network has been proposed by Alan Turing in his 1948 paper “Intelligent Machinery” in which he called them “B-type unorganized machines”.

Artificial visual network models lies in the fact that they can be used to interpret a function from perceptions and execute relevant action. Unsupervised neural networks model can be used to tech representations of the input that catch the salient tendencies of the input circulation, e.g., see the Boltzmann machine (1983), and more recently, deep learning algorithms, which can essentially learn the distribution function of the observed data. Learning in neural networks is principally effective in applications where the complexity of the data or task makes the design of such functions by hand impractical. See Wikipedia for more

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