Screen 1 of 3 - Read before you experiment
Fine-tuning is a choice about which parameters are allowed to change
Frozen stages preserve pretrained representations and require no gradient update. Unfreezing later stages increases adaptation capacity, memory use, and the risk of overwriting useful source knowledge.
The best strategy depends on target-data volume and domain similarity. Treat the displayed accuracy and forgetting values as comparative estimates, not measured training results.
Evidence levelArchitecture-accurate parameter counts + clearly labelled heuristic outcomes
Screen 2 of 3 - Experiment
Run, inspect, and compare
Follow the three guided moves above. Change one variable at a time so every visual change has a clear cause.
Screen 3 of 3 - Consolidate
By the end of this lesson, you will be able to:
- Identify frozen and trainable blocks in a pretrained neural-network architecture.
- Compare feature extraction, partial fine-tuning, and full fine-tuning strategies.
- Select an adaptation strategy that balances target accuracy, data volume, and forgetting risk.
Covers pretrained models and adaptation strategies. Explains feature extraction, partial, and full fine-tuning. Highlights catastrophic forgetting and best practices.