Short Definition
Hidden layers are layers between input and output that transform data representations.
Why It Matters
They enable neural networks to learn complex, hierarchical features.
How It Works (Conceptually)
- Each layer learns a new representation
- Deeper layers capture higher-level patterns
Minimal Python Example
Python
hidden_output = activation(weighted_sum)
Common Pitfalls
- Adding depth without purpose
- Assuming deeper is always better
- Ignoring vanishing gradients
Related Concepts
- Dense Layers
- Activation Functions
- Deep Networks
Neural Network Lexicon 