What is Fine-tuning?
Fine-tuning is a technique used in machine learning and deep learning to adapt a pre-trained model to perform specific tasks or domains. It involves taking a pre-existing model that has been trained on a large dataset and further training it on a smaller, task-specific dataset to make it more accurate and relevant for the target task.The process of fine-tuning begins with a pre-trained model, which is typically trained on a large and diverse dataset allowing the model to learn general patterns and features from the data. However, these models may not be directly suitable for specific tasks or domains. To make the model more task-specific, fine-tuning involves training the pre-trained model on a smaller dataset that is representative of the target task. This dataset may have labeled examples or annotations specific to the task at hand. During fine-tuning, the parameters of the pre-trained model are adjusted using the new dataset, allowing the model to learn task-specific patterns and improve its performance.Related terms
Not to be confused with:
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