What is Transfer Learning?
Transfer learning is a machine learning technique that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. In the context of supervised learning, it leverages pre-trained models that can significantly reduce training time and improve performance on specific tasks.
How Transfer Learning Works
In transfer learning, a model developed for a specific task is reused as the starting point for a model on a second task. This is particularly useful in situations where the second task has limited labeled data, allowing developers to fine-tune the model efficiently.
Benefits
- Reduced Training Time: Using a pre-trained model saves time in training and tuning.
- Improved Performance: Models often perform better when built upon existing knowledge rather than training from scratch.
- Resource Efficiency: Transfer learning enables more efficient use of computational resources.
Applications
Transfer learning is widely applied in various domains such as natural language processing, image recognition, and speech recognition. For instance, models like BERT and ResNet are commonly utilized across different tasks to achieve state-of-the-art results.
Conclusion
Overall, transfer learning represents a powerful approach in supervised learning that enhances model development in machine learning, particularly in resource-constrained environments.