Fine-Tuning (in Machine Learning)

Fine-tuning is an optimization technique applied post-training to refine a machine learning model's performance on specific datasets or tasks. This process entails taking a pre-trained model—often trained on large datasets—and incrementally training it further using smaller task-specific datasets. By fine-tuning the model's parameters, practitioners can significantly improve performance without requiring extensive computational resources typically needed for training from scratch.