EDGE OSDEFINITION
Transfer learning is a machine learning technique where you can reuse a model that you have already trained to train for another similar task. It helps to save time when training new models as instead of training from scratch, you can apply the knowledge gained by your pre-existing model.
This can help speed up training, reduce data needed and lower the overall cost of training. However, transfer learning is commonly used within deep learning, computer vision and NLP and within any AI applications within enterprises, as creating a model from scratch can be very resource intensive.
Why is Transfer Learning Important?
- Saves time and lowers the cost of training.
- Utilizes pre-trained models effectively.
- Less task-specific data is required.
- Improves performance for similar tasks.
Applications of Transfer Learning
- Tailoring pre-trained language models to suit business needs.
- Image recognition systems.
- AI applications for specific industries.
- Improving sentiment analysis and document classification.
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