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Deep Learning Fundamentals

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    Deep Learning Fundamentals > Unit 7 > Unit 7.6
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    Unit 7.6 Leveraging Pretrained Models with Transfer Learning

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    What we covered in this video lecture

    Since labeled data is typical scarce, In practice, it makes sense to leverate pretrained models that we can further finetune on our target data. This process is called transfer learning, and it’s an extremely popular and successful approach for both image and text data.

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    Quiz: 7.6 Leveraging Pretrained Models with Transfer Learning - Part 1

    Transfer learning typically involves:

    Correct. Pretraining helps the model to learn general features that can be useful for other tasks.

    Incorrect. From scratch does not involve using a pretrained model.

    Incorrect. This is a different approach to improve model performance and is not specific to transfer learning.

    Incorrect. While some adjustments to the pretrained model may be made, transfer learning generally involves using the pretrained model as a feature extractor or finetuning it.

    Please answer all questions to proceed.

    Quiz: 7.6 Leveraging Pretrained Models with Transfer Learning - Part 2

    When using transfer learning, fine-tuning refers to:

    Incorrect. This is not specific to finetuning in transfer learning.

    Incorrect. This would not involve using a pretrained model.

    Incorrect. This refers to the process of pretraining, not finetuning.

    Correct. Finetuning refers to the process of retraining some or all layers of a pretrained model on the target task to adapt it to the specific problem.

    Please answer all questions to proceed.

    Quiz: 7.6 Leveraging Pretrained Models with Transfer Learning - Part 3

    In which of the following scenarios is transfer learning most beneficial?

    Incorrect. In this case, transfer learning may not provide significant benefits, as the knowledge gained from the source task may not be applicable to the target task.

    Incorrect. While transfer learning can still be beneficial in this case, having a large dataset for the target task would also allow for training a model from scratch with good performance.

    Incorrect. In this scenario, transfer learning may not provide significant benefits, as the pretrained model’s knowledge from the source task may not be relevant to the target task.

    Correct. This allows the pretrained model to leverage the knowledge it has already gained from the source task, improving performance on the target task.

    Please answer all questions to proceed.
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    Unit 7.6

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