# Evidence: Gradient checking and numerical accuracy

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

## Evidence required

Save analytic and numerical derivatives, the step-size/error table with dtype, the nonquadratic transfer check, and the omitted-term failure/repair. Add the one-sided slopes showing why a symmetric probe cannot certify differentiability at a kink.

- Command: `python3 phases/04-optimization/02-gradient-checking-and-numerical-accuracy/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