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Perceptron game: Difference between revisions

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(Created page with " In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a model which can decide whether an input belongs...")
 
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     Neural Networks work the same way as the perceptron. Perceptron is a single layer neural network and a multi-layer perceptron is called Neural Networks.
 
     Neural Networks work the same way as the perceptron. Perceptron is a single layer neural network and a multi-layer perceptron is called Neural Networks.
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    The evolution of the artificial neuron has progressed through several stages. The roots of which, are firmly grounded within neurological work done primarily by Santiago Ramon y Cajal and Sir Charles Scott Sherrington .  Ramon y Cajal was a prominent figure in the exploration of the structure of nervous tissue and showed that, despite their ability to communicate with each other, neurons were physically separated from other neurons.  With a greater understanding of the basic elements of the brain, efforts were made to describe how these basic neurons could result in overt behaviors, to which William James was a prominent theoretical contributor.
 
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    Working from the beginnings of neuroscience, Warren McCulloch and Walter Pitts  in their 1943 paper, "A Logical Calculus of Ideas Immanent in Nervous Activity," contended that neurons with a binary threshold activation function were analogous to first order logic sentences.
 

Revision as of 19:29, 21 February 2019

   In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a model which can decide whether an input belongs to some specific class.

   Neural Networks work the same way as the perceptron. Perceptron is a single layer neural network and a multi-layer perceptron is called Neural Networks.
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