Description: Neural networks for regression use layers of interconnected nodes to predict continuous values.
Key Points:
- Capable of learning non-linear relationships.
- Requires large amounts of data and computational power.
- Can be prone to overfitting.
Applications: Energy consumption forecasting, algorithmic trading, weather prediction.