Transfer Learning
Transfer Learning leverages knowledge from pre-trained models on large datasets and adapts them to new, related tasks. It's how most modern AI applications are built.
Why Transfer Learning? Training from scratch requires massive data and compute. Transfer learning lets you achieve great results with limited resources.
Common Approaches
Feature Extraction
Freeze pre-trained layers and only train new layers on top.
- Fast training
- Works with small datasets
- Example: Use BERT embeddings
Fine-Tuning
Unfreeze some layers and retrain with a lower learning rate.
- Better performance
- Needs more data
- Example: Fine-tune GPT for chatbot
Transfer Learning Workflow
This pseudocode shows the typical transfer learning process.
python
Output:
Click "Run Code" to see output
Popular Pre-trained Models
- Vision: ResNet, EfficientNet, Vision Transformer (ViT)
- NLP: BERT, GPT, T5, RoBERTa
- Multimodal: CLIP, Flamingo