Transfer Learning

Transfer learning is one of the most powerful and practical techniques in deep learning. Instead of training a model from scratch, you start with a model that has already been trained on a large dataset and fine-tune it for your specific task. This dramatically reduces the amount of data and compute needed.

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Why Transfer Learning?

Challenge	Without Transfer Learning	With Transfer Learning
Data requirements	Need millions of labelled examples	A few hundred or thousand examples may suffice
Training time	Days or weeks on GPUs	Hours or less
Compute cost	Extremely expensive	Affordable
Performance	Often suboptimal with limited data	Often state-of-the-art even with small datasets

The Key Insight

Deep learning models learn hierarchical features. In a CNN trained on ImageNet:

• Early layers learn universal features — edges, textures, colours
• Middle layers learn general patterns — shapes, parts
• Later layers learn task-specific features — faces, cars, dogs

The universal features learned in early and middle layers are useful for virtually any vision task. Transfer learning reuses these features.

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