Transfer Learning
Transfer learning is the practice of taking a model already trained on one task or dataset and reusing or adapting it for another. It speeds development and often improves performance when data for the new task is limited.
In Simple Terms
Think of it as a chef who already knows cooking: you teach them your menu instead of teaching them to cook from zero.
Detailed Explanation
Pre-trained models (e.g., on large text or image corpora) capture general patterns; fine-tuning or adapter layers then specialize them for your domain or task. Transfer learning is standard in NLP and vision and is increasingly used for multimodal and domain-specific apps. Benefits include faster training, better results with less data, and lower compute than training from scratch. The choice of what to freeze versus fine-tune affects performance and cost.
Related Terms
Artificial Intelligence
The simulation of human intelligence processes by machines, especially computer systems.
Read moreMachine Learning
A subset of AI that enables systems to learn and improve from experience without being explicitly programmed.
Read moreBias in AI
Bias in AI is systematic error or unfairness in how a model treats individuals or groups, often reflecting skewed data or flawed design. It can worsen existing inequalities if left unchecked.
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