Adjusting a pre-trained model to perform better on specific tasks or datasets.
More about Fine-Tuning:
Fine-tuning is the process of making small adjustments to a pre-trained model to improve its accuracy on a particular set of data or tasks. This is often done by continuing the training process with a smaller, more specific dataset, allowing the model to better adapt to the nuances of the new information.
Frequently Asked Questions
Why is fine-tuning important in AI?
Fine-tuning is crucial because it helps to adapt a general-purpose model to perform better on tasks specific to certain industries or data types.
How is fine-tuning different from training a model from scratch?
Fine-tuning starts with a model that has already learned general features from a large dataset, whereas training from scratch involves building a model's knowledge base from the ground up.
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