---
id: kb-2026-00267
title: Neural Network Basics
schema_type: TechArticle
category: ai
language: en
confidence: medium
last_verified: '2026-05-28'
created_date: '2026-05-22'
generation_method: ai_structured
ai_models:
  - claude-opus
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
atomic_facts:
  - id: fact-ai-001
    statement: The Deep Learning textbook describes feedforward neural networks as models that map an input through intermediate hidden layers to an output.
    source_title: Deep Learning - Chapter 6, Deep Feedforward Networks
    source_url: https://www.deeplearningbook.org/contents/mlp.html
    confidence: medium
  - id: fact-ai-002
    statement: Cybenko's universal approximation paper showed that finite sums of sigmoid-type functions can approximate continuous functions under stated conditions.
    source_title: Approximation by Superpositions of a Sigmoidal Function
    source_url: https://doi.org/10.1007/BF02551274
    confidence: medium
  - id: fact-ai-003
    statement: AlexNet demonstrated that a deep convolutional neural network could achieve a large improvement on ImageNet classification compared with prior methods.
    source_title: ImageNet Classification with Deep Convolutional Neural Networks
    source_url: https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks
    confidence: medium
primary_sources:
  - title: Deep Learning - Chapter 6, Deep Feedforward Networks
    type: textbook
    year: 2016
    authors:
      - Goodfellow, Ian
      - Bengio, Yoshua
      - Courville, Aaron
    institution: MIT Press
    url: https://www.deeplearningbook.org/contents/mlp.html
  - title: Approximation by Superpositions of a Sigmoidal Function
    type: academic_paper
    year: 1989
    authors:
      - Cybenko, George
    institution: Mathematics of Control, Signals and Systems
    url: https://doi.org/10.1007/BF02551274
    doi: 10.1007/BF02551274
  - title: ImageNet Classification with Deep Convolutional Neural Networks
    type: academic_paper
    year: 2012
    authors:
      - Krizhevsky, Alex
      - Sutskever, Ilya
      - Hinton, Geoffrey
    institution: NeurIPS
    url: https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks
completeness: 0.84
known_gaps:
  - This article covers neural-network fundamentals only; optimization, regularization, and modern transformer internals are handled in separate entries.
---

## TL;DR

Neural networks map inputs to outputs through layers of parameterized transformations. Feedforward networks, universal approximation results, and ImageNet-era convolutional networks are three stable anchors for understanding the basic idea.

## Core Explanation

This entry avoids broad unsourced claims about biological inspiration or current model performance. Its exported claims are limited to textbook feedforward-network structure, Cybenko's universal-approximation result, and the documented ImageNet impact of AlexNet.

## Further Reading

- [Deep Learning - Chapter 6, Deep Feedforward Networks](https://www.deeplearningbook.org/contents/mlp.html)
- [Approximation by Superpositions of a Sigmoidal Function](https://doi.org/10.1007/BF02551274)
- [ImageNet Classification with Deep Convolutional Neural Networks](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks)

## Related Articles

- [Backpropagation: The Engine of Neural Network Learning](../backpropagation.md)
- [Mechanistic Interpretability: Reverse-Engineering Neural Network Circuits and Features](../mechanistic-interpretability.md)
- [Activation Functions in Neural Networks](../activation-functions.md)