Fine Tuning

Definition

Fine tuning is the process of adapting a pre-trained machine learning model to a new task using additional training on smaller, domain-specific datasets.

Purpose

The purpose is to reuse knowledge from large models and improve performance on specialized tasks with fewer resources.

Importance

• Reduces training cost and time compared to building models from scratch.
• Improves performance on domain-specific tasks.
• Risks overfitting if training data is too narrow.
• Related to transfer learning.

How It Works

1. Select a pre-trained base model.
2. Replace or adjust task-specific layers.
3. Train with labeled data from the new domain.
4. Adjust learning rates to balance old and new knowledge.
5. Validate and test for generalization.

Examples (Real World)

• BERT fine-tuned for sentiment analysis.
• GPT models fine-tuned for customer support chatbots.
• Vision models fine-tuned for medical imaging classification.

References / Further Reading

• Howard & Ruder. “Universal Language Model Fine-tuning” (ULMFiT). ACL 2018.
• Hugging Face Transformers Documentation.
• Pan & Yang. “A Survey on Transfer Learning.” IEEE TKDE.