# AI1.4 Fine Tuning

This skill focuses on techniques and strategies for fine-tuning pre-trained AI models in HPC environments. It includes adapting models to domain-specific data, optimizing resource usage, and applying transfer learning for efficient training.

## Requirements

* External: Understanding of basic deep learning and model training processes
* Internal: None

## Learning Outcomes

* Explain the purpose and benefits of fine-tuning pre-trained models.
* Identify key hyperparameters and architectural considerations during fine-tuning.
* Apply methods for efficient fine-tuning, including layer freezing and learning rate scheduling.
* Describe how fine-tuning strategies differ for large-scale models on HPC infrastructure.
* Recognize potential pitfalls such as overfitting, catastrophic forgetting, and data leakage.

** Caution: All text is AI generated **