Basic Math Concepts – Mean Square Error usage in Neural Network Use Cases
Before diving into MSE and regression neural networks, learners should be familiar with:
- Basic Arithmetic – addition, multiplication
- Mean (Average) – summing values and dividing by count
- Exponentiation – squaring values
- Linear Equations – y=mx+b form (used in neurons)
- Understanding Error – difference between actual and predicted
Summary
| Concept | Explanation |
|---|---|
| Loss Function | Measures how far predictions are from real outputs |
| MSE | Best for regression-type outputs |
| Mathematical Form | Average of squared differences |
| When to Use | Predicting real, continuous numbers |
Mean Square Error usage in Neural Network Use Cases – Visual Roadmap