# Evidence: Linear algebra and loss intuition

Copy this template into your own portfolio and fill it after running your modified exercise.

## Evidence required

Keep the row-by-row predictions, analytic weight/bias derivatives, pairwise-broadcast counterexample, and checked-loss rejection/repair. Include the analytic vector-gradient derivation and mean-versus-sum comparison.

- Command: `python3 phases/04-optimization/01-linear-algebra-and-loss-intuition/code/main.py`
- Working directory: repository root
- Python / JAX / NumPy versions: 
- Hardware and device: 
- My prediction before running: 
- My change to the exercise: 
- Exit code and meaningful output: 
- Why the result supports or contradicts my prediction: 
- Checkpoint result: 
- Artifact link or saved filename: 
- Portfolio status: saved in local workspace