Transfer learning is a machine learning technique where a model trained on one task is used as a starting point for a different but related task. The idea behind transfer learning is that knowledge gained from solving one problem can be applied to another related problem, allowing the model to learn more efficiently with fewer training examples. This approach is commonly used in image and natural language processing tasks, where pre-trained models are fine-tuned on specific tasks to achieve high accuracy with less training data.
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