Principles of Data Science
Word embeddings are numerical representations of words in a continuous vector space, where words with similar meanings are mapped to nearby points. This technique allows for capturing semantic relationships and contextual similarities between words, making them essential for various natural language processing tasks. By transforming words into vectors, word embeddings facilitate more efficient computation and enhance the understanding of language by models.
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