When dealing with a small dataset and wanting to leverage the knowledge from a model trained on a larger dataset, which approach would be most suitable?
- Fine-tuning
- Transfer Learning
- Random Initialization
- Gradient Descent Optimization
The most suitable approach for leveraging knowledge from a model trained on a larger dataset with a small dataset is "Transfer Learning." It involves adapting the pre-trained model to the new task.
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