无监督学习Unsupervised Learning

不给答案，让机器自己在数据里找结构。No answers given — the machine finds structure in the data by itself.

训练数据没有标注答案，模型自己找分组、压缩或规律。

聚类和降维是它的两类经典任务。

Training data carries no labels; the model discovers groupings, compressions or patterns on its own.

Clustering and dimensionality reduction are its two classic task families.

嵌入向量、数据分群背后常用它。它也解释了为什么没标注的数据也有价值。Embeddings and data grouping often rest on it. It also explains why unlabeled data still has value.

去 Models 图谱：看这个模型有哪些量化版、按你的显存该下哪一档Models atlas: which quantisations exist and which tier fits your VRAM ↗

回到名词地图看第1站的其他 34 个词 →Back to the map: the other 34 terms in station 1 →

• scikit-learn 无监督学习官方教程scikit-learn unsupervised learning tutorial ↗