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Related Questions
- What is the general approach to pre-training and fine-tuning in transfer learning, and how do these steps impact the complexity of the model?
- How does pre-training with a large corpus affect the model's ability to generalize to new tasks and domains?
- Can you explain the concept of knowledge distillation and its role in reducing the model size and computational resources required for fine-tuning?
- How does the choice of pre-training task and fine-tuning objective influence the trade-off between model size and computational resources?
- What are some strategies for selecting the optimal pre-training and fine-tuning settings to balance model size and computational resources?
- How does the use of transfer learning impact the model's ability to adapt to new tasks and domains, and what are the implications for model size and computational resources?
- Can you discuss the relationship between the number of parameters and the computational resources required for training and inference in pre-trained and fine-tuned models?
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