Transfer Learning
ELI5 — The Vibe Check
Transfer Learning is using knowledge a model already has from one task to help it with a different task. A model that learned to understand images of dogs can transfer that knowledge to recognize cats much faster. It's like a chef who already knows French cuisine picking up Italian much faster than someone starting from zero.
Real Talk
Transfer learning is a machine learning paradigm where a model trained on one task is adapted to a different but related task. The pre-trained model's learned representations are reused, typically through fine-tuning. It dramatically reduces data and compute requirements and is the basis for the modern 'pre-train then fine-tune' paradigm.
When You'll Hear This
"Transfer learning lets us train on only 1000 examples." / "Fine-tuning is a form of transfer learning."
Related Terms
Deep Learning
Deep Learning is Machine Learning that's been hitting the gym.
Fine-tuning
Fine-tuning is like taking a smart graduate student who knows everything and then sending them to a specialist bootcamp.
Model
A model is the trained AI — the finished product.
Pre-training
Pre-training is the first massive phase where an AI reads basically the entire internet and learns to predict the next word billions of times.
Weights
Weights are the numbers inside a neural network that determine what it knows and how it behaves — they're the AI's 'brain cells.