What is the difference between fine-tuning and pre-training in AI?

What is the difference between fine-tuning and pre-training in AI?

Pre-training involves training a model on a large dataset to learn general features, while fine-tuning adjusts it on a smaller, specific dataset for a particular task.

Can you give an example of when to use fine-tuning?

Sure! If you have a language model pre-trained on general text, you might fine-tune it on medical texts to improve its performance in healthcare applications.

Is fine-tuning always necessary?

Not always. If your task is similar to the pre-training data, you might achieve good results without fine-tuning.

What are the benefits of pre-training?

Pre-training allows models to learn a wide range of features, making them more adaptable and effective for various tasks.

How long does fine-tuning usually take?

The duration of fine-tuning can vary widely, from a few hours to several days, depending on the dataset size and model complexity.