Transfer learning is a technique where knowledge a model gained solving one task is reused as a starting point for a related task. Instead of training from scratch, you build on a model that already learned general patterns, then adapt it with a smaller dataset. This saves time and data, and it is why powerful AI can be tailored to niche problems without enormous training resources.
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