What Is Transfer Learning?
What Is Transfer Learning?
Transfer learning is a machine learning technique where knowledge gained from solving one problem (the source task) is reused to solve a different but related problem (the target task). In this approach, you first train a model on a source domain (DS), which consists of the original data and task. Afterwards, you apply the learned knowledge to a target domain (DT), which involves new data and a potentially different but related task.
For instance, consider how learning to ride a bicycle helps you when you try to ride a motorcycle. The balance and coordination skills you developed for one task can be transferred and adapted to the new, but related, activity. This intuitive example captures the essence of transfer learning: leveraging prior experience to accelerate and improve learning in a new context.
The significance of transfer learning in machine learning comes from its ability to reduce the need for large labeled datasets in the target domain. When you have limited data for your new task but abundant data for a related one, transfer learning allows you to build effective models more efficiently. This approach not only speeds up training but also often leads to better performance, especially in scenarios where collecting new data is costly or impractical.
Transfer learning is especially useful when the target task has limited data, but the source task has abundant data and similar structure.
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What Is Transfer Learning?
Transfer learning is a machine learning technique where knowledge gained from solving one problem (the source task) is reused to solve a different but related problem (the target task). In this approach, you first train a model on a source domain (DS), which consists of the original data and task. Afterwards, you apply the learned knowledge to a target domain (DT), which involves new data and a potentially different but related task.
For instance, consider how learning to ride a bicycle helps you when you try to ride a motorcycle. The balance and coordination skills you developed for one task can be transferred and adapted to the new, but related, activity. This intuitive example captures the essence of transfer learning: leveraging prior experience to accelerate and improve learning in a new context.
The significance of transfer learning in machine learning comes from its ability to reduce the need for large labeled datasets in the target domain. When you have limited data for your new task but abundant data for a related one, transfer learning allows you to build effective models more efficiently. This approach not only speeds up training but also often leads to better performance, especially in scenarios where collecting new data is costly or impractical.
Transfer learning is especially useful when the target task has limited data, but the source task has abundant data and similar structure.
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