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Related Questions
- What are the primary benefits of using transfer learning in machine learning for adapting to new domains and tasks?
- How does transfer learning help in reducing the risk of overfitting in new tasks and domains?
- Can you provide examples of successful applications of transfer learning in real-world scenarios?
- What are some common challenges that arise when adapting a pre-trained model to a new domain or task?
- How do you determine the optimal level of fine-tuning required for a pre-trained model to adapt to a new task or domain?
- What are some potential limitations of transfer learning in terms of performance and accuracy in new domains and tasks?
- Can transfer learning be used for adapting to new tasks with significantly different data distributions or feature sets?
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