What are the benefits of using pretrained models in machine learning?
Pretrained models save time and resources, improve performance, and require less data for training.
Can you explain how they improve performance?
They leverage knowledge from large datasets, allowing them to generalize better on new tasks.
Are there any specific examples of pretrained models?
Yes, models like BERT for NLP and ResNet for image classification are popular examples.
What should I consider when choosing a pretrained model?
Consider the model's architecture, the domain it was trained on, and its compatibility with your task.
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