Universal approximation theorem

The Universal Approximation Theorem is a foundational result in the field of neural networks and approximation theory. It states that a feedforward neural network with at least one hidden layer and a finite number of neurons can approximate any continuous function on a compact subset of \(\mathbb{R}^n\) to any desired degree of accuracy, provided that the activation function used in the network is non-constant, bounded, and continuous.