Meta-Learning

Overview

A subfield of machine learning focused on developing systems that can learn how to learn more efficiently. Meta-learning models learn patterns and strategies across different learning tasks, allowing them to adapt rapidly to new tasks with fewer examples or less training data. This approach is used for building models that generalize well and that can rapidly adapt to new use cases, by learning the "learning process" itself.

What is Meta-Learning?

Systems that learn to learn more efficiently.

How does it work?

Learns patterns across different learning tasks.

Applications?

Few-shot learning, rapid adaptation to new tasks.

Related Concepts

• Few-Shot Learning
• Transfer Learning
• Neural Network
• Continuous Learning