# Transfer Learning: Stand on a Pretrained Model

> Source: <https://dev.to/dev48v/transfer-learning-stand-on-a-pretrained-model-4hba>
> Published: 2026-06-26 15:42:29+00:00

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](https://dev48v.infy.uk/dl/day17-transfer-learning.html)

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?

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.

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](https://dev48v.infy.uk/dl/day17-transfer-learning.html)

Part of DeepLearningFromZero. 🌐 [https://dev48v.infy.uk](https://dev48v.infy.uk)