Transfer Learning
Technical Definition
Transfer Learning is a machine learning technique where a model developed for one task is reused as the starting point for a model on a second, related task. Instead of training a model from scratch, transfer learning enables leveraging a pre-trained model that has already learned useful patterns and representations from a large dataset. This approach significantly reduces training time and improves performance, especially when data is scarce for the target task.
Real-World Applications
Transfer learning is widely used in image recognition tasks, where a model pre-trained on a large dataset like ImageNet can be fine-tuned to identify specific objects (e.g., medical imaging for tumor detection). In NLP, models like BERT or GPT are pre-trained on large corpora and then fine-tuned for specific tasks like sentiment analysis or question answering.
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