What is fine-tuning in transfer learning?

Can you explain what fine-tuning in transfer learning is?

Sure! Fine-tuning is a process where a pre-trained model is further trained on a specific dataset to improve its performance on a particular task.

Why is fine-tuning important in machine learning?

Fine-tuning allows models to adapt to new tasks with less data and computational resources, leveraging knowledge from previous training.

What are some common applications of fine-tuning?

Common applications include image classification, natural language processing, and speech recognition, where pre-trained models are adapted for specific tasks.

How does fine-tuning differ from training a model from scratch?

Fine-tuning uses a pre-trained model as a starting point, while training from scratch requires building a model from the ground up, often needing more data and time.