What are some common mistakes in fine-tuning AI models?
An example of an AI chatbot conversation about 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.
How can I avoid overfitting when fine-tuning?
To avoid overfitting, use techniques like dropout, early stopping, and regularization.
Is it important to have a diverse dataset?
Yes, a diverse dataset helps the model generalize better and perform well on unseen data.
What role does hyperparameter tuning play?
Hyperparameter tuning is crucial as it can significantly impact the model's performance and learning efficiency.
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