Training
Fine-tuning
Adapting pre-trained models to specific tasks
What is Fine-tuning?
Fine-tuning is the process of taking a pre-trained model and continuing its training on a new, typically smaller dataset for a specific task. This allows the model to adapt its learned features to the nuances of the new task while retaining general knowledge.
Key Points
1
Continues training from pre-trained weights
2
Adapts to specific domains
3
Often uses lower learning rates
4
More efficient than training from scratch
Practical Examples
Fine-tuning GPT for specific domains
Adapting vision models to medical images
Customizing chatbots
Task-specific BERT variants