An embedding identifies one mathematical structure with a structure-preserving copy inside another. In topology it is a homeomorphism onto its image with the subspace topology; an algebraic embedding is an injective homomorphism. A smooth embedding additionally has injective differential everywhere, so its image is an embedded submanifold.
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Embedding, in the context of machine learning and natural language processing (NLP), refers to a technique used to represent items, such as words, entities, or even entire documents, in a continuous vector space. These vectors can capture semantic meanings and relationships between the items, allowing for effective analysis and processing. ### Key Points about Embeddings: 1. **Dense Representation**: Unlike traditional representations (e.g., one-hot encoding), embeddings provide a more compact and informative representation.