Transfer learning is a powerful technique in machine learning where a model trained on one task is reused as the starting point for a model on a related task. This approach saves time and computational resources by leveraging pre-trained knowledge.

🌱 Key Concepts

  • Pretrained Models: Models like BERT, ResNet, or GPT-3 are trained on massive datasets for general tasks
  • Feature Extraction: Using base layers of a pretrained model to extract features for new tasks
  • Fine-tuning: Adjusting the final layers of a pretrained model for specific applications

📊 Applications

Domain Use Case Example
Computer Vision Image classification with limited data Customizing ResNet for medical imaging
Natural Language Processing Text summarization Fine-tuning BERT for legal document analysis
Speech Recognition Voice assistant customization Adapting Wav2Vec2 for customer service chats

🧪 Implementation Steps

  1. Select a Pretrained Model
    Pretrained Model
  2. Freeze Base Layers
    Feature Extraction
  3. Add Task-specific Layers
    Fine-tuning Architecture
  4. Train & Evaluate
    Training Process

📘 Further Reading

For a deeper dive into practical implementations:
Advanced Transfer Learning Techniques

Transfer Learning Diagram