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 a model trained on general image recognition (e.g., ImageNet) and fine-tuning it for detecting diseases in X-rays or identifying plant species.
- Natural Language Processing (NLP): Adapting pretrained language models like BERT or GPT for tasks like sentiment analysis, question answering, or translation.
- Speech Recognition: Using a model trained on broad speech patterns and adapting it to a specific accent or language.
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