Neural Network
A computing system inspired by biological brains, consisting of interconnected nodes (neurons) that process information in layers.
In-Depth Explanation
Neural networks are the foundation of modern AI. Inspired by biological brains, they consist of interconnected nodes (artificial neurons) organised in layers that progressively transform inputs into useful outputs.
Structure of a neural network:
- Input layer: Receives raw data
- Hidden layers: Transform data through learned patterns
- Output layer: Produces final predictions
- Weights: Learned values controlling connection strength
- Activation functions: Non-linear transformations enabling complex learning
Types of neural networks:
- Feedforward: Simple layer-to-layer processing
- Convolutional (CNN): Specialised for images
- Recurrent (RNN): Handle sequential data
- Transformer: Power modern LLMs
- Generative adversarial (GAN): Two networks competing
How learning happens:
- Forward pass: Input processed through layers
- Loss calculation: Compare output to desired result
- Backpropagation: Calculate how each weight contributed to error
- Weight update: Adjust weights to reduce error
- Repeat millions of times
The key insight: neural networks learn representations - they discover useful features from raw data rather than requiring hand-engineered rules.
Business Context
All modern AI including LLMs is built on neural networks. Understanding the basics helps you communicate with AI vendors and teams.
How Clever Ops Uses This
While you don't need to build neural networks to use AI effectively, understanding the basics helps us communicate architecture decisions and trade-offs to our Australian business clients.
Example Use Case
"A neural network learns to recognise patterns by adjusting connections between artificial neurons through training on millions of examples."