What is the process of fine-tuning in neural networks, and when is it appropriate to use?

Fine-tuning is the process of taking a pre-trained model and making small adjustments to its weights on a new, typically smaller, dataset. It is performed after an initial training phase on a large dataset, leveraging the learned representations for similar tasks. Fine-tuning is appropriate when computational resources are limited, and when you have a small dataset where training a model from scratch would lead to overfitting or underperforming. This approach allows you to achieve better performance in a shorter time by adapting the model to specific data characteristics while retaining the generalized features learned during pre-training.