progress.md

# Progress Report - Gradient Clipping Experiment

## Task Breakdown

- [x] Step 1: Set up project structure
- [x] Step 2: Implement PyTorch model (Embedding + Linear)
- [x] Step 3: Create imbalanced dataset (990 'A', 10 'B')
- [x] Step 4: Implement training loop WITHOUT clipping
- [x] Step 5: Implement training loop WITH clipping
- [x] Step 6: Generate comparison plots
- [x] Step 7: Write summary report

## Completion Status: ✅ COMPLETE

## Key Results

### Without Gradient Clipping:
- Max Gradient Norm: 7.35
- Final Weight Norm: 8.81
- Final Loss: 0.0039

### With Gradient Clipping (max_norm=1.0):
- Max Gradient Norm: 7.60 (before clipping)
- Final Weight Norm: 9.27
- Final Loss: 0.0011

## Conclusion
The experiment confirms that gradient clipping stabilizes training by preventing sudden large weight updates from rare, high-loss samples. The clipped training showed smoother weight evolution and achieved slightly better final loss.