Word embeddings in a vector space
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Word embeddings in a vector space: similar words lie close together and relations appear as parallel offsets (king − man + woman ≈ queen)
This illustration explains word embeddings in a vector space: similar words lie close together and relations appear as parallel offsets (king − man + woman ≈ queen). Handy for explaining semantic similarity in machine learning presentations, theses, posters and course notes. Editable SVG from the Machine Learning Pro pack, with grouped layers you can recolor.
Format: SVG (editable vector). License: Sciicons Pro license. Courtesy: Sciicons. Part of the Machine Learning pack.
Keywords: Machine Learning Pack, Sciicons Pro, word embeddings, embeddings, vector space, word2vec, king queen man woman, semantic similarity, machine learning, artificial intelligence, ai, deep learning, data science
