# Convolutional Neural Networks (CNN)
Status: public
Confidence: medium (0.76) (verified)
Last verified: 2026-05-28
Generation: ai_structured

## TL;DR
Convolutional neural networks are neural networks designed for grid-like data such as images. They use local filters, shared weights, nonlinear layers, and often pooling or striding to learn visual features across spatial positions.

## Core Explanation
Early layers often detect local patterns, while deeper layers combine them into more task-specific features. CNN history includes document-recognition systems, ImageNet-scale classifiers, biomedical segmentation networks, and residual architectures that made very deep networks easier to train.

## Detailed Analysis
CNNs are still widely used because convolution builds in useful assumptions about locality and translation. Modern systems may combine CNNs with attention, transformers, pretraining, or task-specific heads, so claims should specify the architecture and task rather than treating all visual AI as one model family.

## Further Reading
- LeCun et al. on document recognition
- AlexNet
- ResNet

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