Inference
Unsupervised Learning
A category of machine learning where models learn patterns from data without labeled examples or explicit correct answers.
In depth
A category of machine learning where models learn patterns from data without labeled examples or explicit correct answers. The model discovers structure on its own, such as clusters, correlations, or compressed representations. LLM pre-training is a form of unsupervised (or self-supervised) learning, where the model learns to predict the next token from massive amounts of unlabeled text.
Example
The model discovers structure on its own, such as clusters, correlations, or compressed representations.
Go deeper at Developers Digest
Hands-on guides, comparisons, and tutorials that cover Inference.
FAQ
Common questions
What is Unsupervised Learning?
A category of machine learning where models learn patterns from data without labeled examples or explicit correct answers.
Why does Unsupervised Learning matter for AI developers?
Unsupervised Learning sits in the Inference part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Unsupervised Learning?
Developers Digest publishes tutorials and videos that cover Inference topics including Unsupervised Learning. Check the blog and YouTube channel for hands-on walkthroughs.
Related
Related terms
Inference
In-Context Learning
The ability of a language model to learn new tasks from examples or instructions provided in the prompt, without any weight updates or training.
Inference
Multi-Token Prediction (MTP)
A training objective where a language model learns to predict the next several tokens at each position instead of only the next one, using extra output heads on a shared model trunk.
Inference
Next-Token Prediction
The training objective behind GPT-style language models: given a sequence of tokens, predict a probability distribution over the token that comes next.
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