Neural Network

A computing system inspired by biological brains, using layers of interconnected nodes to learn patterns from data.

Neural networks are computational systems that learn to recognize patterns by adjusting connections between layers of artificial neurons.

Structure

• Input layer: Receives raw data
• Hidden layers: Extract increasingly abstract features
• Output layer: Produces predictions or classifications

How They Learn

1. Data flows forward through the network
2. Output is compared to desired result
3. Error is propagated backward (backpropagation)
4. Connection weights are adjusted to reduce error
5. Repeat with more data

Types

• Feedforward: Information flows one direction
• Convolutional (CNN): Specialized for images
• Recurrent (RNN): Handles sequential data
• Transformer: Uses attention mechanisms

Deep Learning

“Deep” neural networks have many hidden layers, enabling them to learn complex hierarchical representations—the foundation of modern AI breakthroughs.