What is the difference between fine-tuning and transfer learning?

What is the difference between fine-tuning and transfer learning?

Fine-tuning is a specific type of transfer learning where a pre-trained model is further trained on a new dataset.

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

Sure! If you have a model trained on general images, you might fine-tune it on a specific dataset of cats.

Is transfer learning always better than training from scratch?

Not necessarily. Transfer learning is often faster and requires less data, but it depends on the task and data availability.

What are the benefits of using fine-tuning?

Fine-tuning can lead to better performance on specific tasks and reduces the time and resources needed for training.