Transfer Learning

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Transfer learning is a technique where you take a model that has already learned useful patterns from a large task and reuse it for a different, smaller task. Instead of starting from scratch, you start with a model that already “knows” general features, like edges and shapes in images, or common word patterns in language. In deep learning, this usually means loading a network that was trained on a huge dataset, such as ImageNet for vision or a large text corpus for NLP, and then adapting it to your own domain.

In practice, teams load the pre-trained model, keep most of its layers as they are, and replace the final layers with new ones suited to the target task. Sometimes early layers are frozen at first, and later unfrozen for more precise tuning. This approach saves a lot of time, requires far less labeled data, and often leads to better performance than training a smaller model from scratch.

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