# Universal Approximation Theorem

> Source: https://sukruyusufkaya.com/en/glossary/universal-approximation-theorem
> Updated: 2026-05-13T20:00:13.596Z
> Type: glossary
> Category: derin-ogrenme
**TLDR:** A theoretical result stating that a neural network with sufficient capacity can approximate a very broad class of functions.

<p>The Universal Approximation Theorem is one of the core theoretical foundations explaining why neural networks are such powerful representational tools. Under suitable conditions, it states that even a single-hidden-layer network can approximate many continuous functions. However, this result does not guarantee that learning will be easy or that the model will train efficiently; it only provides a framework about representational capability.</p>