Embeddings and Representation Learning

An Embedding is a learned numerical representation created through representation learning. Each item becomes a vector, allowing systems to estimate relationships such as semantic similarity.

Japanese terms

1. Embedding — 埋うめ込こみ: A learned numerical representation that places items in a vector space.
2. Representation learning — 表現学習ひょうげんがくしゅう: Learning useful features or representations directly from data.
3. Vector — ベクトル: An ordered collection of numbers used to represent position, direction, or learned features.
4. Semantic similarity — 意味的類似度いみてきるいじど: A measure of how closely two items match in meaning rather than surface form.

Related topics

Embeddings support retrieval-augmented generation and alignment between modalities in multimodal AI.