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Word2Vec is a popular algorithm used in natural language processing (NLP) to create a vector representation of words (i.e., numerical values in a high-dimensional space where each dimension represents a feature of the word). This vector representations of words is used to derive relationships between words and to analyze text data.

Word2Vec has many applications, including natural language processing tasks such as text classification, information retrieval, and machine translation. It is also often used as a tool for text analytics and language modeling.

The Word2Vec algorithm is based on the idea that words that appear in similar contexts tend to have similar meanings, and therefore, can be represented by similar vectors in the high-dimensional space.

Storing words as vectors means that cosine similarity can be used to perform various tasks such as finding words that are most similar to a given word, detecting relationships between words (such as antonyms and synonyms), and clustering words based on their semantic similarity. The cosine similarity between two word vectors can be calculated by taking the dot product of the vectors and dividing by the product of their magnitudes. The resulting value ranges between -1 and 1, where values closer to 1 indicate a higher degree of similarity between the two words.

There are two main architectures for Word2Vec: Continuous Bag of Words (CBOW) and Skip-gram. CBOW predicts a target word based on the context words surrounding it, while Skip-gram predicts the context words based on the target word.

Word2Vec and its two primary architectures CBOW and Skipgram.