Word Embeddings (Word2Vec, GloVe)

Summary

Dense vector representations of words that capture semantic meaning (king - man + woman ≈ queen) — a major leap over BoW/TF-IDF and the conceptual bridge to modern embeddings.

• 1Load pretrained Word2Vec or GloVe vectors and explore nearest-neighbor words.
• 2Try the classic vector-arithmetic examples to build intuition.
• 3Understand why static embeddings (one vector per word) were later replaced by contextual embeddings.