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Transfer Learning
Reusing a model trained on one task as the starting point for a second related task.
Why It Was Important
Researchers realized that training models from scratch required too much data. By taking a model already trained to recognize basic shapes on massive datasets, and just 'fine-tuning' the final classification layers on a much smaller dataset (like classifying specific medical x-rays), AI became democratized for organizations without Google-scale data.
Who Invented It
Machine Learning Community
Academics shifting from bespoke training to model reusability.
Applications
- Medical Imaging
- Fine-tuning
- Rapid Prototyping
Key Papers
- A Survey on Transfer Learning
Sinno Jialin Pan, Qiang Yang · IEEE Transactions on Knowledge and Data Engineering · 2010
Videos
Transfer Learning (C3W2L07)
DeepLearningAI
What is Transfer Learning?
Hugging Face