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/Transfer Learning

Transfer Learning

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Transfer Learning

Transfer learning is like learning to play a new musical instrument when you already know another one. If you play guitar and want to learn ukulele, you don't start from zero—you already understand chords, rhythm, and finger positioning. Similarly, transfer learning takes a model trained on one task and applies that knowledge to a new, related task.

Everyday example: Imagine you're an experienced chef specializing in Italian cuisine. When asked to cook Thai food, you adapt your skills to new ingredients and techniques.

Why it matters: Training models from scratch requires enormous data and computing power. Transfer learning lets you create powerful models with much less, making advanced AI accessible to more people.

Transfer learning is a machine learning technique where a model developed for one task is repurposed for a second task, significantly reducing training time and data requirements.

How it works:

  1. Select a pre-trained model: For example, ResNet, BERT, or VGG trained on large datasets.
  2. Freeze early layers: Preserve universal feature detectors.
  3. Replace and retrain later layers: Replace final layers with task-specific ones and train only them.
  4. Fine-tuning (optional): Unfreeze some layers and train the entire network at a very low learning rate.

Common approaches: Feature extraction, fine-tuning, and domain adaptation.

Real-world applications: Medical imaging, sentiment analysis, and wildlife conservation.