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Answer: AutoML supports transfer learning by using techniques such as feature extraction, where the pre-trained model is used to extract relevant features from the data, and fine-tuning, where the pre-trained model is adapted to the target dataset by training the final layers on the new data.
Transfer learning is a technique that leverages pre-trained models to improve the performance of machine learning models, especially when dealing with limited data or computational resources. AutoML can support transfer learning by using techniques such as feature extraction, where the pre-trained model is used to extract relevant features from the data, and fine-tuning, where the pre-trained model is adapted to the target dataset by training the final layers on the new data. These techniques help to reduce training time and improve model generalization by leveraging the knowledge learned from the pre-trained model. Option C correctly describes the techniques used by AutoML for transfer learning and their significance in improving model performance.
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In the context of AutoML, explain the role of transfer learning and how AutoML can leverage pre-trained models to improve the performance of the models it generates. Provide a detailed explanation of the techniques used by AutoML for transfer learning and their significance in reducing training time and improving model generalization.
A
AutoML does not support transfer learning, as it relies on training models from scratch for each dataset.
B
AutoML supports transfer learning by fine-tuning pre-trained models on the target dataset, but it does not provide any specific techniques for this process.
C
AutoML supports transfer learning by using techniques such as feature extraction, where the pre-trained model is used to extract relevant features from the data, and fine-tuning, where the pre-trained model is adapted to the target dataset by training the final layers on the new data.
D
AutoML supports transfer learning by automatically selecting a pre-trained model that is similar to the target task and using it as a starting point for training the new model.
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