What is transfer learning?
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What is Transfer Learning?
Transfer learning is a machine learning technique where a model developed for one task is reused as the starting point for a different but related task. Instead of training a model from scratch (which requires large datasets and high computational power), you take a pre-trained model and adapt it to your specific problem.
Key Idea
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Many models (like those trained on ImageNet for images or BERT/GPT for NLP) learn general features (edges, shapes, word embeddings) that are useful across tasks.
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You can reuse these learned features and fine-tune the model on your smaller, domain-specific dataset.
Many models (like those trained on ImageNet for images or BERT/GPT for NLP) learn general features (edges, shapes, word embeddings) that are useful across tasks.
You can reuse these learned features and fine-tune the model on your smaller, domain-specific dataset.
Types of Transfer Learning
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Feature Extraction – Use the pre-trained model as a fixed feature extractor; only train a new classifier on top.
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Fine-Tuning – Unfreeze some (or all) layers of the pre-trained model and retrain them on your new dataset for better adaptation.
Feature Extraction – Use the pre-trained model as a fixed feature extractor; only train a new classifier on top.
Fine-Tuning – Unfreeze some (or all) layers of the pre-trained model and retrain them on your new dataset for better adaptation.
Benefits
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Requires less data and less computation.
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Faster training compared to building a model from scratch.
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Often leads to better accuracy when data is limited.
Requires less data and less computation.
Faster training compared to building a model from scratch.
Often leads to better accuracy when data is limited.
Examples
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Using a pre-trained ResNet model (trained on millions of images) to classify medical X-rays.
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Adapting BERT (trained on large text corpora) for tasks like sentiment analysis or question answering.
Using a pre-trained ResNet model (trained on millions of images) to classify medical X-rays.
Adapting BERT (trained on large text corpora) for tasks like sentiment analysis or question answering.
✅ In short: Transfer learning means leveraging knowledge learned from one problem and applying it to another, making machine learning faster and more efficient.
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