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What is Transfer Learning?
Transfer learning is a machine learning technique that leverages knowledge learned from one task or domain and applies it to another related task or domain. It allows the transfer of learned representations, features, or models from a source task to a target task, thereby reducing the need for extensive training on the target task.
In transfer learning, a pre-trained model is used as a starting point, which has been trained on a large dataset or a different but related task. The pre-trained model captures general patterns, features, or representations that are useful across tasks or domains. Instead of starting from scratch, the pre-trained model is adapted or fine-tuned on the target task using a smaller labeled dataset specific to the target domain. This process helps the model to quickly learn task-specific nuances or details, improving its performance on the target task with fewer training examples.Transfer learning is particularly beneficial when the target task has limited labeled data or when training a model from scratch is computationally expensive or time-consuming. By utilizing the pre-trained model, transfer learning enables the efficient use of existing knowledge and accelerates the learning process for the target task.
There are different approaches to transfer learning, depending on the similarity between the source and target tasks. In some cases, the entire pre-trained model can be used as a feature extractor, where the learned representations from the earlier layers are utilized as input features for the target task. In other cases, only a portion of the pre-trained model is used, and additional layers or modules are added to fine-tune the model specifically for the target task.
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