Neural Networks: The Universality Theorem

Visual Proof of Universal Approximation Theorem for Neural Networks
Updated 18 Jul 2018

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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.

Cite As

Mayank Jhamtani (2024). Neural Networks: The Universality Theorem (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2018a
Compatible with any release
Platform Compatibility
Windows macOS Linux
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