Transfer Learning

In more detail

Transfer learning leverages pre-trained models and adapts them for new tasks. Instead of training from scratch, you start with learned features from similar tasks. This reduces training time, data requirements, and computational cost. Widely used in computer vision and NLP.

Examples

• Using ImageNet pre-trained models for custom image classification
• Fine-tuning BERT for specific text tasks
• Domain adaptation in machine translation

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