Applying knowledge gained from training on one task to a different but related task, dramatically reducing training time and data requirements.
Transfer learning leverages pre-trained models (like GPT or BERT) and adapts them to specific domains. Instead of training from scratch, you start with a model that already understands language or images and fine-tune it on your smaller, specialized dataset.
Taking a pre-trained BERT model and fine-tuning it on your company's support tickets to build a specialized intent classifier.
Transfer learning makes AI accessible to businesses that don't have millions of training examples — you can build effective models with hundreds or thousands of domain-specific samples.
Teaching AI models new tasks with just a few examples.
Adapting a pre-trained AI model to specific tasks or domains by training it on specialized data.
A training technique where AI models are refined based on human preferences and evaluations of their...
A machine learning approach where models learn from labeled training data — input-output pairs that ...
A machine learning approach where models discover patterns and structure in data without labeled exa...
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