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Transfer Learning

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Transfer learning is the strategy of reusing knowledge a model gained on one task to tackle a new, related one, instead of starting from scratch. A model trained on large amounts of general data has already learned useful features, like recognizing shapes or linguistic structures, that can be adapted to a specific problem with little extra data. It's like an expert pianist who learns the organ far faster than an absolute beginner: much of the basic skill transfers, hugely cutting learning time. In practice you start from a pre-trained model and fine-tune it toward the new goal.

Definition

Transfer learning is a pillar of modern AI: it makes excellent results possible with modest datasets and limited budgets, democratizing access to powerful models. It's the logic behind fine-tuning foundation models and explains why complex models are now rarely trained fully from scratch.

Related terms

• Fine-tuning
• Pre-training
• Foundation Model
• Catastrophic Forgetting
• Knowledge Distillation

More in AI Fundamentals

• LLM (Large Language Model)
• Token
• Context Window
• Temperature
• AI Hallucination
• Fine-tuning
• RAG (Retrieval Augmented Generation)
• Embedding

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