Term tag: transfer learning

Fine-tuning (FT)

What fine-tuning changes inside a model, what it costs in GPU memory, and why it reliably buys format and style but not new facts.

Foundation Models

Not a marketing word for a big AI model: a foundation model is trained once on broad data and reused as the base for many downstream tasks.

Knowledge Distillation

How a small model learns what a big one knows: the teacher's full probability distribution carries structure the hard labels do not — Hinton's dark knowledge.

Meta-Learning

Learning to learn: training a model across thousands of tiny tasks so it adapts from a handful of examples — and how that differs from fine-tuning.

Pre-trained Models

A neural network someone else already trained on a huge general dataset, which you download and adapt to your task instead of training from scratch.

Representation Learning

Representation learning is machine learning that discovers the useful features of raw data automatically, instead of humans hand-engineering them.

Transfer Learning (TL)

Transfer learning reuses a model trained on one large task as the starting point for a related task, so you need far less labeled data and compute.

Zero-shot Learning

Performing a task with zero examples, on prior knowledge alone — and the split that matters: zero-shot prompting versus classical zero-shot learning.