Universal Approximators Theorem

George Cybenko publishes earliest version of the Universal Approximation Theorem in his paper “Approximation by superpositions of a sigmoidal function“. He proves that feed forward neural network with single hidden layer containing finite number of neurons can approximate any continuous function. It further adds credibility to Deep Learning.

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