You don't have a million labeled images or a GPU farm — and you don't need them. Transfer learning lets you stand on a model someone else trained and reach high accuracy with a few examples in minutes. Here's the idea, visualized.
♻️ Race scratch vs transfer: https://dev48v.infy.uk/dl/day17-transfer-learning.html
The insight
The early layers of a trained network learn general features — edges, textures, shapes — that are useful for almost any vision task. Only the last layers are task-specific. So why relearn edges from scratch?
Two ways to do it
- Feature extraction: freeze the pretrained backbone, replace the final classifier with a small new "head," and train only the head on your data. Fast, needs little data.
- Fine-tuning: also unfreeze the top few backbone layers and train them at a low learning rate so you adapt without wrecking what they learned.
The demo races two accuracy curves: "from scratch" crawls up and plateaus low (not enough data); "transfer learning" starts high and climbs fast. Tweak the example count and freeze/fine-tune to see them respond.
Why it matters now
This is exactly why fine-tuning an open LLM works: a foundation model already learned language; you adapt it cheaply. Transfer learning is what makes deep learning practical for the rest of us.
🔨 Full recipe (load pretrained → freeze → new head → train → optionally fine-tune low-LR) on the page: https://dev48v.infy.uk/dl/day17-transfer-learning.html
Part of DeepLearningFromZero. 🌐 https://dev48v.infy.uk
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