How Machine Learns Using Neural Network

The Universal Approximation Theorem, first demonstrated by George Cybenko in 1989 and later refined by Kurt Hornik in 1991, forms the theoretical foundation of machine learning. This theorem states that a feedforward neural network with a single hidden layer can approximate any continuous function to arbitrary precision, given the right activation function and a sufficient number of neurons in the hidden layer.