Fine-Tuning

AI Terms

Fine-Tuning

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:

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.
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.
What are some challenges in fine-tuning?
- Challenges include finding a suitable pre-trained model, adjusting the hyperparameters, and avoiding overfitting.

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