Fine-tuning in machine learning refers to the process of adapting a pre-trained model to a new, specific task by retraining it on a smaller dataset. This technique offers an efficient solution for building new models with limited resources. By fine-tuning, machine learning practitioners can create highly specialized models that excel in domain-specific tasks.
Fine-tuning involves loading a pre-trained model and freezing certain layers to preserve the general knowledge learned from previous training. The process is particularly effective for task-specific models, such as text summarization and code generation, as well as domain-specific tasks like medical image classification.
While fine-tuning can lead to impressive results, it also poses challenges. Overfitting can occur when working with small datasets, requiring ML practitioners to experiment with layers and find the right balance between generalization and specificity.