Universal Approximation Theorem
Version 1.0.0 · Updated 2026-07-30
CORE DEFINITION
A feedforward neural network with at least one hidden layer and a nonlinear activation function can approximate any continuous function to arbitrary accuracy, provided that the number of neurons is sufficiently large. Scaffold role: The mathematical cornerstone of AI capabilities. It theoretically proves that the potential of neural networks is unlimited. With sufficient data and computational power (neurons), there is nothing mathematically 'unlearnable'. This explains why deep learning can dominate various fields.
SCAFFOLDING EFFECT
Reduce cognitive load
The mathematical cornerstone of AI capabilities. It theoretically proves that the potential of neural networks is unlimited. With sufficient data and computational power (neurons), there is nothing mathematically 'unlearnable'. This explains why deep learning can dominate various fields.
Anchor fast decisions
A feedforward network with one hidden layer can approximate any continuous function when the width is sufficient.
MINIMUM ACTION
In progress 0/1Practice this model in one real situation:
account_treeGenealogyexpand_more
menu_bookReferencesexpand_more
Source support: Explicit
- zh.wikipedia.orghttps://zh.wikipedia.org/wiki/%E9%80%9A%E7%94%A8%E8%BF%91%E4%BC%BC%E5%AE%9A%E7%90%86verified
PRIVATE NOTES · Only visible to you
SAVED Q&A
ENTRY Q&A · Private saving available
Ask with a clear boundary
thinkingmodels answers from published entry context only.
Your question is sent to thinkingmodels. The answer uses public entry context only.
RELATED MODELS