Neural Network
A computing system inspired by biological neural networks that learns patterns from data.
Definition
Neural networks are computational models inspired by human brain structure, consisting of interconnected nodes (neurons) organized in layers. Input data flows through the network, with each connection having a weight that adjusts during training to improve predictions.
Modern neural networks range from simple architectures for basic classification to complex deep networks with billions of parameters. They excel at pattern recognition, prediction, and generating new content.
Why It Matters
Neural networks underpin most modern AI applications. Understanding their capabilities and limitations helps businesses evaluate AI solutions and set realistic expectations.
While powerful, neural networks require training data, computational resources, and expertise. Their "black box" nature also raises interpretability challenges in regulated industries.
Examples in Practice
An e-commerce platform uses neural networks to power recommendation engines, analyzing purchase history to suggest products with 40% higher conversion rates.
Financial institutions deploy neural networks for fraud detection, identifying suspicious patterns that rule-based systems miss.
A content platform uses neural networks for personalization, dynamically adjusting what each user sees to maximize engagement.