Description

Fine-tuning is a transfer learning technique where a pre-trained model is further trained on a specific task or dataset. This process allows the model to adapt its learned features to the new task, often resulting in better performance than training from scratch, especially when the target task has limited labeled data. Fine-tuning is commonly used with large language models and in computer vision tasks.

Examples

• 😃 Fine-tuning BERT for sentiment analysis
• 🖼️ Adapting a pre-trained image classification model to a new set of categories

Applications