The Universal Approximation Theorem is a foundational result in the field of neural networks and deep learning, particularly relevant to the study and application of artificial neural networks. This theorem essentially states that a feedforward neural network with a single hidden layer containing a finite number of neurons can approximate any continuous function on compact