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.