Neural networks are math models loosely inspired by how brain neurons connect. Layers of nodes that learn patterns.

The Basic Structure

Input layer: Your data goes in (pixels, numbers, text)

Hidden layers: Where the "magic" happens—patterns are learned

Output layer: Final prediction comes out

How They Learn

1. Start with random weights (connections between nodes)
2. Make predictions (probably wrong at first)
3. Calculate error (how wrong was it?)
4. Adjust weights to reduce error
5. Repeat millions of times

Why "Deep" Learning?

More hidden layers = deeper network. Each layer learns increasingly complex patterns:

• Layer 1: Edges and lines
• Layer 2: Shapes and textures
• Layer 3: Object parts
• Layer 4: Whole objects

What They're Good At

• Image recognition
• Speech recognition
• Natural language (ChatGPT uses neural networks)
• Playing games
• Anything with complex patterns

The Trade-off

Pros: Extremely powerful for complex problems
Cons: Need lots of data, expensive to train, "black box" (hard to explain why they work)