78. Word Embeddings: Words as Numbers That Actually Mean Something

Short summary

Word embeddings solve the meaning problem by representing tokens as dense vectors that cluster semantically similar words. This tutorial walks through Word2Vec implementation in PyTorch with a complete skip-gram training loop, demonstrating how context-based learning produces vector spaces where 'cat' and 'dog' are neighbors and semantic relationships can be computed through vector algebra (king - man + woman ≈ queen).

• •Word embeddings transform tokens into dense vectors where semantic meaning emerges from context similarity
• •Skip-gram training maps words that co-occur to similar vector positions through contrastive loss
• •Vector space operations like algebra (king - man + woman ≈ queen) reveal learned semantic relationships

Generated with AI, which can make mistakes.