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Word2Vec — Word Embeddings (Explained)
Learn how Word2Vec creates vector embeddings and why semantics emerge from co-occurrence.
What you'll learn
- How embeddings represent words as vectors.
- CBOW vs Skip-gram training objectives.
- Why negative sampling makes training efficient.
Embeddings: meaning as geometry
Word2Vec learns a vector for each word so that words used in similar contexts end up near each other.
This creates a geometry where similarity and analogies can emerge.
CBOW vs Skip-gram
CBOW predicts a target word from surrounding context words.
Skip-gram predicts surrounding context words from a target word and often performs well on smaller datasets.
Negative sampling
Instead of normalizing across the entire vocabulary, negative sampling trains against a small set of “wrong” words per update.
This drastically speeds up training on large corpora.
Key takeaways
- Word2Vec produces dense word vectors from co-occurrence.
- CBOW and Skip-gram trade speed vs representation quality.
- Negative sampling makes large-vocab training practical.
- Modern Transformers provide contextual embeddings, but Word2Vec remains useful.
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