How does transfer learning primarily benefit deep learning models in terms of training time and data requirements?
- Increases training time
- Requires more data
- Decreases training time
- Requires less data
Transfer learning benefits deep learning models by decreasing training time and data requirements. It allows models to leverage pre-trained knowledge, saving time and reducing the need for large datasets. The model starts with knowledge from a source task and fine-tunes it for a target task, which is often faster and requires less data than training from scratch.
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