What are the best practices for transfer learning?
Transfer learning involves taking a pre-trained model and fine-tuning it for a specific task. Best practices include selecting a relevant pre-trained model, freezing early layers, and using a smaller learning rate.
How do I choose the right pre-trained model?
Choose a model that has been trained on a similar task or dataset. Consider the architecture and performance metrics relevant to your specific application.
What should I do if my model is overfitting?
If your model is overfitting, try techniques like data augmentation, dropout, or reducing the model complexity. You can also gather more training data.
Can I use transfer learning for image classification?
Absolutely! Transfer learning is particularly effective for image classification tasks, as many models are pre-trained on large image datasets.
What are some common pitfalls to avoid?
Common pitfalls include using an irrelevant pre-trained model, not fine-tuning enough layers, and not validating the model properly.
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