What are the challenges of using pretrained models in AI?
Using pretrained models can lead to issues like domain mismatch, overfitting, and lack of interpretability.
Can you explain domain mismatch?
Domain mismatch occurs when the data used to train the model differs significantly from the data it encounters in real-world applications.
How can I mitigate overfitting when using these models?
You can mitigate overfitting by fine-tuning the model on your specific dataset and using techniques like dropout or regularization.
Are there any specific pretrained models you recommend?
It depends on your task, but popular models include BERT for NLP and ResNet for image classification.
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