--- title: Word2vec : A Deep learning approach of word embeddings description: An introductory guide to Word2Vec, Google's NLP technique for representing words as dense vectors in a continuous space. Covers the two core architectures —... canonical: https://daily.dev/posts/word2vec-a-deep-learning-approach-of-word-embeddings-whzcy8nb5 twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Word2vec : A Deep learning approach of word embeddings | daily.dev og:description: An introductory guide to Word2Vec, Google's NLP technique for representing words as dense vectors in a continuous space. Covers the two core architectures —... og:url: https://daily.dev/posts/word2vec-a-deep-learning-approach-of-word-embeddings-whzcy8nb5 og:image: https://api.daily.dev/og/posts/WhZcY8NB5.png og:image:alt: Word2vec : A Deep learning approach of word embeddings og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Word2vec : A Deep learning approach of word embeddings **[Medium](https://daily.dev/sources/medium_js)** · 5 min read · 0 upvotes · 0 comments ## Summary An introductory guide to Word2Vec, Google's NLP technique for representing words as dense vectors in a continuous space. Covers the two core architectures — CBOW (Continuous Bag of Words) and Skip-gram — explaining how each works, their trade-offs, and when to use them. Includes a hands-on Python code example using the gensim library to train a Word2Vec model on a small corpus and retrieve word vectors and similar words. ## Full article daily.dev links to this article rather than hosting it. 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