The Universal Approximation Theorem states that a neural network with at least one hidden layer of a sufficient number of neurons, and a non-linear activation function can approximate any continuous function to an arbitrary level of accuracy.

It is a fundamental result in the field of ANN, which states that certain types of neural network can approximate certain function to any desired degree of accuracy. This theorem suggest that a neural network is capable of learning complex patterns and relationships in data as long as certain conditions are fulfilled.

The Universal Approximation Theorem tells us that Neural Networks has a kind of universality i.e. no matter what f(x) is, there is a network that can approximately approach the result and do the job! This result holds for any number of inputs and outputs.

Deep convolutional neural networks (CNN or DCNN) are the type most commonly used to identify patterns in images and video. DCNNs have evolved from traditional artificial neural networks, using a three-dimensional neural pattern inspired by the visual cortex of animals.