Transfer Learning

TrainingAdvanced

Definition

A machine learning method where a model developed for a specific task is reused as the starting point for a model on a second, different but related task. This is the core principle behind using pre-trained foundation models and then fine-tuning them, saving significant time and computational resources.

Why "Transfer Learning" Matters in AI

Understanding transfer learning is essential for anyone working with artificial intelligence tools and technologies. This training-related concept is crucial for understanding how AI models learn and improve over time. Whether you're a developer, business leader, or AI enthusiast, grasping this concept will help you make better decisions when selecting and using AI tools.

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Related terms

Fine-tuningFoundation ModelTrainingNeural Network

Frequently Asked Questions

What is Transfer Learning?

A machine learning method where a model developed for a specific task is reused as the starting point for a model on a second, different but related task. This is the core principle behind using pre-t...

Why is Transfer Learning important in AI?

Transfer Learning is a advanced concept in the training domain. Understanding it helps practitioners and users work more effectively with AI systems, make informed tool choices, and stay current with industry developments.

How can I learn more about Transfer Learning?

Start with our AI Fundamentals course, explore related terms in our glossary, and stay updated with the latest developments in our AI News section.

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