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  "path": "/dev48v/transfer-learning-stand-on-a-pretrained-model-4hba",
  "publishedAt": "2026-06-26T15:42:29.000Z",
  "site": "https://dev.to",
  "tags": [
    "machinelearning",
    "deeplearning",
    "ai",
    "beginners",
    "https://dev48v.infy.uk/dl/day17-transfer-learning.html",
    "https://dev48v.infy.uk"
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  "textContent": "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.\n\n♻️ **Race scratch vs transfer:** https://dev48v.infy.uk/dl/day17-transfer-learning.html\n\n##  The insight\n\nThe 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?\n\n##  Two ways to do it\n\n  * **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.\n  * **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.\n\n\n\nThe 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.\n\n##  Why it matters now\n\nThis 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.\n\n🔨 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\n\nPart of DeepLearningFromZero. 🌐 https://dev48v.infy.uk",
  "title": "Transfer Learning: Stand on a Pretrained Model"
}