AI Glossarytechniques

Transfer Learning

A machine learning technique where a model trained on one task is adapted to perform a different but related task, reducing the data and compute needed.

How It Works

Transfer learning is the fundamental principle behind modern AI. Instead of training a model from scratch for your specific task (which requires massive data and compute), you take a pre-trained model and adapt it. Every time you fine-tune GPT or use Llama for a custom task, you are using transfer learning. The pre-training phase (which costs millions of dollars and months of GPU time) gives the model general language understanding. Transfer learning lets you leverage that investment by adapting the model to your domain with relatively little data. Fine-tuning a model on 100 medical documents transfers its general language abilities to medical text understanding. For builders, transfer learning means you never start from zero. The practical implication: fine-tuning with as few as 10-50 examples can produce a specialized model. Even prompt engineering is a form of zero-data transfer learning, where you transfer the model's broad capabilities to your specific task through instructions alone.

Common Use Cases

  • 1Domain-specific model adaptation
  • 2Fine-tuning with limited data
  • 3Cross-lingual model transfer
  • 4Adapting vision models to custom image tasks

Related Terms

Large Language Model (LLM)

A neural network trained on massive text datasets that can generate, understand, and reason about human language.

Fine-Tuning

The process of further training a pre-trained AI model on your specific data to improve performance on domain-specific tasks.

Few-Shot Learning

A prompting technique where you provide a small number of input-output examples in the prompt to teach the model the desired behavior.

Reinforcement Learning from Human Feedback (RLHF)

A training technique that aligns AI model behavior with human preferences by using human feedback to reward desired outputs and penalize undesired ones.

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