Transfer Learning
A technique where a model trained on one task is reused as the starting point for a model on a second related task, dramatically reducing training time and data requirements.
Detailed Explanation
Transfer Learning leverages knowledge gained from solving one problem and applies it to a different but related problem. Instead of training a model from scratch, you start with a pre-trained model (often trained on millions of examples) and fine-tune it for your specific task with much less data. This is analogous to how humans apply knowledge from one domain to learn faster in another. Transfer learning has democratized AI by making powerful models accessible to organizations without massive datasets or computing resources.
Real-World Examples
Medical Image Classification
HealthcareHospitals use models pre-trained on millions of general images, then fine-tune with just 1,000 medical images to achieve 92% diagnostic accuracy—versus needing 100,000+ images from scratch.
Sentiment Analysis
E-commerceCompanies fine-tune pre-trained language models with just 500 industry-specific reviews to achieve 88% accuracy in sentiment classification, saving 6 months of development time.
Frequently Asked Questions
Q:How much data do I need with transfer learning?
Typically 10-100x less than training from scratch. For image tasks, you might need hundreds instead of millions of examples. For text, a few hundred to a few thousand examples often suffice.
Related Terms
Fine-tuning
The process of taking a pre-trained AI model and further training it on a specific dataset to specialize it for a particular task, industry, or use case.
Few-Shot Learning
A machine learning approach where models learn to perform new tasks from just a few examples, rather than requiring thousands of training samples.
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