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Rule of Thumb in Transfer Learning

Explore the essential guidelines for applying transfer learning effectively by understanding when and how to fine-tune models. This lesson helps you decide the best approach based on your new dataset's size and similarity to the original, covering strategies to avoid overfitting and adjusting learning rates for improved model performance.

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Points to consider

There might be a variety of questions in your mind such as:

  1. When do you fine-tune?
  2. How do you fine-tune?
  3. How do you decide what type of transfer learning you should perform on a new dataset?

The answer to the above questions depends on two factors: the size of the new dataset and its similarity to the original dataset on which your pre-trained model was trained. We have listed four common rules that you should keep in mind while performing transfer learning strategies. These are:

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