A company is using domain-specific models. The company wants to avoid creating new models from the beginning. The company instead wants to adapt pre-trained models to create models for new, related tasks. Which ML strategy meets these requirements?
B: Use transfer learning.
Explanation:
Transfer learning is a machine learning strategy that leverages pre-trained models and adapts them to new but related tasks. This allows the company to avoid building models from scratch, significantly reducing the time and resources required for training. By fine-tuning the pre-trained model on domain-specific data, the company can achieve high performance for the new task without starting from the beginning.
Transfer learning involves taking a pre-trained model, which has been trained on a large dataset, and adapting it to a new, related task. This approach offers several advantages:
TL where a model pre-trained on one task is fine-tuned for a new, related task.
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