Notes:
The word2vec algorithm is a machine learning algorithm that uses a neural network model to learn associations between words from a large corpus of text. The algorithm is trained on the corpus, and uses the relationships between words in the corpus to generate a vector representation of each word. These vectors can then be used to compute similarity between words, predict missing words in a sentence, or classify words into categories.
The word2vec algorithm is a type of word embedding algorithm, which means that it converts words into numerical vectors that can be used as input to other machine learning algorithms. The vectors generated by the word2vec algorithm are typically high-dimensional, meaning that they capture a lot of information about the relationships between words. This makes them useful for a variety of natural language processing tasks, such as sentiment analysis or document classification.
Wikipedia:
See also:
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