Question: Explain the concept of fine-tuning in transfer learning and when it is commonly applied.

Answer: Fine-tuning in transfer learning involves taking a pre-trained model and further training it on a specific task or dataset. It is commonly applied when the target task is closely related to the source task, and the pre-trained model has already learned useful features. Fine-tuning can improve performance on the target task with less training data.