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Related Questions
- What is the concept of fine-tuning in machine learning, and how is it used to adapt pre-trained models to new tasks?
- How does the pre-trained model's weights get updated during the fine-tuning process, and what determines the new weight values?
- Can you explain the difference between fine-tuning and retraining a model from scratch, and under what circumstances would each method be preferred?
- What role does the task-specific data play in fine-tuning a pre-trained model, and how does it influence the adaptation of the model's weights?
- How does the fine-tuning process balance the trade-off between preserving the pre-trained model's knowledge and adapting to the new task requirements?
- What are some common techniques used to fine-tune pre-trained models, such as transfer learning or multi-task learning, and how do they impact the adaptation of the model's weights?
- Can you provide an example of fine-tuning a pre-trained model on a specific task, such as natural language processing or computer vision, and explain the key steps involved in the fine-tuning process?
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