Neural Networks & AI
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Neural Networks & AI
A neural network is a series of algorithms that actions to recognize fundamental relationships in a set of data through a progression that imitates the way the human brain functions. A neural network is a network or circuit of neurons, or in a contemporary sense, an artificial neural network, composed of artificial neurons or nodes. Neural networks can become accustomed to changing input; so the network generates the best imaginable result without necessitating to redesigning the output criteria.
Thus a neural network is either a natural biological neural network, made up of real biological neurons, or an artificial neural network, for solving artificial intelligence (AI) problems. These artificial networks may possibly be used for predictive modeling, adaptive control and applications where they can be taught via a dataset. Self-learning resulting from understanding can transpire within networks, which can develop conclusions or assumptions from a multifaceted and superficially distinct set of information.
Neural networks were first proposed in 1944 by Warren McCullough and Walter Pitts, two University of Chicago researchers who moved to MIT in 1952 as founding members of what’s sometimes called the first cognitive science department. Neural nets were a most important area of research in both neuroscience and computer science untiln1969, when, according to computer science lore, they were killed off by the MIT mathematicians Marvin Minsky and Seymour Papert, who a year later would become co-directors of the new MIT Artificial Intelligence Laboratory. The technique then enjoyed resurgence in the 1980s, fell into eclipse again in the first decade of the new century, and has returned with robust speed in the second, powered largely by the increased processing power of graphics chips.
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