Neural Networks: The Universality Theorem

Visual Proof of Universal Approximation Theorem for Neural Networks

This live script explores the Universal Approximation Theorem which states that a single layer of "artificial neurons" can be used to approximate any function, with an arbitrarily small approximation error. This project presents an intuitive proof of the theorem by means of visual aids. The project allows the user to vary the different network parameters to approximate an arbitrary function f(x). Submitted as part of the MATLAB Online Live Editor Challenge 2018.