What is fine-tuning in AI?

Can you explain what fine-tuning is in the context of AI?

Fine-tuning is the process of taking a pre-trained AI model and adjusting it on a smaller, specific dataset to improve its performance on a particular task.

Why is fine-tuning important for AI models?

Fine-tuning allows models to adapt to specific tasks or domains, enhancing their accuracy and relevance compared to using a general model.

What are some common applications of fine-tuning?

Common applications include natural language processing tasks like sentiment analysis, image classification, and even medical diagnosis.

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

Fine-tuning uses a pre-trained model as a starting point, which saves time and resources, while training from scratch requires building a model from the ground up.