Machine Learning
intermediate
Fine-tuning
The process of further training a pre-trained model on a specific dataset to adapt it for particular tasks.
Detailed Explanation
Fine-tuning is a transfer learning technique where a model pre-trained on a large dataset is further trained on a smaller, more specific dataset to adapt it for a particular task or domain. This approach leverages the general knowledge captured by the pre-trained model while specializing it for specific applications. Fine-tuning typically involves adjusting all or some of the model's parameters using a lower learning rate than the initial training. This technique is widely used with large language models and other deep learning models as it requires significantly less data and computational resources than training from scratch.
Examples
- Fine-tuning BERT for sentiment analysis
- Adapting GPT models for specific writing styles
- Customizing vision models for medical imaging
Tags
transfer learning
model adaptation
specialization