= Continuous embedding
{wiki=Continuous_embedding}
Continuous embedding refers to a representation technique used in machine learning and natural language processing (NLP) where discrete entities, such as words or items, are mapped to continuous vector spaces. This allows for capturing semantic properties and relationships between entities in a way that facilitates various computational tasks. \#\#\# Key Characteristics: 1. **Dense Representations**: Continuous embeddings typically result in dense vectors, meaning that they use lower-dimensional spaces to represent entities compared to one-hot encoding, which results in sparse vectors.
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