🤖 Machine Learning / AI
Intermediate
What is word embedding and what is Word2Vec?
Answer
Word embeddings are dense vector representations of words in a continuous vector space, where semantically similar words are close together. Unlike one-hot encoding (sparse, no semantic meaning), embeddings capture relationships: king - man + woman ≈ queen. Word2Vec (Google, 2013) learns embeddings using two architectures: CBOW (predicts a word from its context) and Skip-gram (predicts context from a word). Other popular embeddings: GloVe (uses global co-occurrence statistics) and FastText (uses character n-grams, handles unknown words).
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