What is transfer learning?
Transfer learning is a machine learning technique that enables data scientists to benefit from the knowledge gained from a previously used machine learning model for a similar task. This learning takes humans’ ability to transfer their knowledge as an example. If you learn how to ride a bicycle, you can drive other 2-wheeled vehicles more efficiently. Similarly, a model trained for cars’ autonomous driving can be used for autonomous driving of trucks.
When to use transfer learning?
Transfer learning is used in the following circumstances:
- There is insufficient data: In some cases, data scientists might not have enough data to train their ML models. Working with insufficient data would result in lower performance; starting with a pre-trained model would help data scientists build much better models.
- There is insufficient time to train: Some ML models can’t be trained easily and can take too long to work properly. When there is not enough time to build a new model or too many ML tasks to handle, data scientists can prefer to adopt a similar pre-trained model. It will save time for building the model instead of creating a new model.
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