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.

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

• 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