What Is Transfer Learning in AI? See Example

Transfer learning is a technique in AI where a pre-trained model is adapted for a new but related task.

Instead of training a model from scratch, transfer learning leverages existing knowledge, saving time and computational resources.

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How Transfer Learning Works

1. Select Pre-trained Model: Choose a model trained on a large dataset (e.g., ImageNet).
2. Adapt Model: Freeze some layers and fine-tune others for the new task.
3. Train on New Data: Use a smaller dataset to train the model on the target task.
4. Evaluate and Deploy: Test the model’s performance and deploy it.

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Advantages of Transfer Learning

• Reduces training time and computational cost
• Requires less labeled data for new tasks
• Improves model performance with prior knowledge
• Enables rapid deployment in real-world applications

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Disadvantages

• Pre-trained models may carry biases from original data
• May not perform well if new task is very different
• Requires careful layer selection and fine-tuning

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Real-World Examples

• Image classification using pre-trained CNNs
• Text classification and sentiment analysis with BERT
• Medical imaging with limited labeled data
• Speech recognition adapting large models to new accents
• Object detection using pre-trained YOLO models

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Conclusion

Transfer learning accelerates AI development by reusing knowledge from existing models, making it a powerful technique for fast and efficient solutions.

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Citations

https://savanka.com/category/learn/ai-and-ml/
https://www.w3schools.com/ai/