What are some common mistakes in fine-tuning AI models?

What are some common mistakes in fine-tuning AI models?

Some common mistakes include overfitting, not using enough data, and ignoring validation metrics.

Can you explain overfitting and how to avoid it?

Overfitting occurs when a model learns noise in the training data instead of the actual patterns. To avoid it, use techniques like regularization, dropout, or early stopping.

What about the importance of validation metrics?

Validation metrics help you assess how well your model generalizes to unseen data. Ignoring them can lead to poor performance in real-world applications.

Are there specific datasets that are better for fine-tuning?

Yes, datasets that are diverse and representative of the target domain are ideal for fine-tuning, as they help the model learn relevant features.