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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.The key advantage of fine-tuning is that it saves significant time and computational resources compared to training a model from scratch. Pre-trained models are already equipped with a good understanding of general patterns, and fine-tuning enables them to adapt quickly to new tasks with fewer training examples.
The process of fine-tuning requires careful consideration of hyperparameters, such as learning rates and regularization techniques, to strike the right balance between preserving the general knowledge of the pre-trained model and adapting it to the new task. It also requires monitoring the performance of the model on the validation set and making adjustments accordingly.
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