Transfer Learning
Transfer learning is a technique in artificial intelligence and machine learning where a model developed for one task is reused as the starting point for a new, related task. Instead of training a model from scratch, transfer learning leverages the knowledge the model has already learned—such as patterns, features, or weights—on a large, general dataset, and fine-tunes it for a more specific or smaller-scale task.
Why It's Useful
Training deep learning models from scratch often requires:
- Massive labeled datasets
- High computational power
- Long training times
Benefits
- Saves time and resources
- Improves performance on small datasets
- Speeds up development cycles
- Enables rapid prototyping
Common Applications
Computer Vision: Using models trained on general image recognition (e.g., ImageNet) fine-tuned for specific tasks.
Natural Language Processing (NLP): Adapting pretrained models like BERT or GPT.
Speech Recognition: Adapting broad speech patterns to specific accents or languages.