Transfer Learning
Transfer learning is a technique where a model trained on one task is repurposed or fine-tuned for a different, but related, task. Instead of training a model from scratch with limited data, you start with a pre-trained model that has already learned general features from a large dataset. This approach leverages existing knowledge, often significantly reducing the amount of data and computational power needed for the new task.
Transfer learning is like learning to play the guitar after already knowing how to play the ukulele, where many finger positions and musical concepts carry over.
It dramatically speeds up development and improves performance on tasks with limited data by leveraging knowledge gained from larger, related problems.
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