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Related Questions
- What are the key differences between transfer learning and domain adaptation, and how do they contribute to effective fine-tuning?
- Can you explain the concept of pre-training and how it enables the adaptation of models to new tasks and domains?
- What are some common techniques used to adapt the training data for domain adaptation, such as data augmentation and curriculum learning?
- How do techniques like knowledge distillation and multi-task learning facilitate the transfer of knowledge from a pre-trained model to a new task?
- What is the role of hyperparameter tuning in the fine-tuning process, and how can it impact the performance of the model?
- Can you discuss the challenges associated with adapting a pre-trained model to a new domain or task, and how to address these challenges?
- What are some best practices for selecting the optimal pre-trained model and training data for fine-tuning, and how to evaluate the performance of the fine-tuned model?
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