Fine-tuning is the process of making small adjustments to the parameters of a pre-trained machine learning model to optimize its performance on a specific task. This technique allows models to leverage previously learned features, which can significantly reduce the time and data needed for training while improving accuracy and efficiency. It is particularly useful in deep learning, where models are often complex and computationally expensive to train from scratch.
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