Definition: Fine-tuning is the process of adapting a pre-trained machine learning model to a specific task or domain.

Detailed Description:

Fine-tuning involves starting with a pre-trained model that has been trained on a large dataset and then adapting it to a new task by training it on a smaller dataset specific to that task. This can be more efficient than training a model from scratch, especially when the new task is similar to the original task the model was trained on.

Fine-tuning is commonly used in natural language processing, computer vision, and other areas where pre-trained models are available.

Related Terms:

• Machine Learning
• Transfer Learning
• Pre-trained Model
• Adaptation

FAQs:

1. What is the difference between training a model from scratch and fine-tuning?

   • Training a model from scratch involves training the model on a large dataset from the beginning, while fine-tuning involves starting with a pre-trained model and adapting it to a new task.

2. When is fine-tuning useful?

   • Fine-tuning is useful when you have a limited amount of data for your specific task and when the task is similar to the task the pre-trained model was originally trained on.

3. What are some challenges in fine-tuning?

   • Challenges include finding a suitable pre-trained model, adjusting the hyperparameters, and avoiding overfitting.