# Transfer Learning (Edge AI Glossary)

## metadata
- term: Transfer Learning
- slug: transfer-learning
- letter: T
- route: /resources/glossary/transfer-learning
- canonical: https://klyff.com/resources/glossary/transfer-learning

## Definition & Context
Transfer Learning is a machine learning technique where a model developed for one task is adapted to perform a related task, reducing the need for large datasets. In Edge AI, it...

## Details
## Transfer Learning

#### What is Transfer Learning?

Transfer Learning is a machine learning technique where a model developed for one task is adapted to perform a related task, reducing the need for large datasets. In Edge AI, it enables devices to leverage pretrained models for faster, efficient on-device intelligence. Synonym: **knowledge transfer**.

#### Why Is It Used?

 
- Reduces training time and computational costs.
- Minimizes data requirements for new tasks.
- Enhances AI performance on resource-constrained edge devices. 

#### How Is It Used?

 
- Deploy a pretrained neural network on edge devices.
- Fine-tune the model using task-specific or local data.
- Integrate with Edge AI platforms for real-time inference and automation. 

#### Types of Transfer Learning

 
- **Inductive Transfer Learning:** Applies knowledge from one domain to a different but related task.
- **Transductive Transfer Learning:** Uses source domain knowledge to improve performance on a similar target domain.
- **Unsupervised Transfer Learning:** Transfers knowledge where labeled data is scarce on the target task. 

#### Benefits of Transfer Learning

 
- Faster deployment of AI solutions at the edge.
- Reduced dependency on large, labeled datasets.
- Optimized model performance for IoT and edge devices.
- Supports real-time decision-making without cloud reliance.