Neural Network

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:

1. Forward pass: Input processed through layers
2. Loss calculation: Compare output to desired result
3. Backpropagation: Calculate how each weight contributed to error
4. Weight update: Adjust weights to reduce error
5. Repeat millions of times

The key insight: neural networks learn representations - they discover useful features from raw data rather than requiring hand-engineered rules.