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Related Questions
- How do task-specific requirements impact the architecture and hyperparameters of a pre-trained model when fine-tuning?
- What are the advantages and disadvantages of fine-tuning a pre-trained model versus training from scratch for specific tasks?
- Can you explain the concept of 'transfer learning' and its role in fine-tuning pre-trained models for new tasks?
- What are the key factors that determine whether to fine-tune a pre-trained model or start from scratch for a specific task?
- How does the size and complexity of the pre-trained model affect its suitability for fine-tuning versus training from scratch?
- What are the implications of task-specific requirements on the choice of pre-trained model architecture and size?
- Can you discuss the trade-offs between the potential benefits of fine-tuning a pre-trained model versus the potential drawbacks of overfitting and underfitting?
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