Transfer Learning
Transfer learning is a machine learning technique where a model trained on one task is reused as a starting point for a model on a different, but related, task. Instead of training a new model from scratch, which requires a large amount of data and computational resources, a pre-trained model's learned knowledge is leveraged. For example, a model trained to identify various objects can be fine-tuned to classify specific types of medical images with much less data.
It's like learning to ride a bicycle and then quickly adapting that skill to ride a scooter, using your existing balance and coordination instead of starting from zero.
It dramatically reduces the data, time, and computational power required to build high-performing models, especially in data-scarce domains.
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