Word Embeddings (Word2Vec, GloVe) — Roadmap Step & Resources
Representing words as dense vectors that capture semantic meaning
- Level: advanced
- Estimated time: 1 days
- Roadmap: Deep Learning & Neural Networks
Before this step
Interview questions
- Why do word embeddings place 'king' and 'queen' close together but in a consistent direction relative to 'man' and 'woman'?
Study resources
- Word2Vec, Clearly Explained!!! (Video) — StatQuest's widely-loved, clear explanation of word embeddings and Word2Vec.
- Word Embeddings Tutorial (Article) — The official PyTorch tutorial on building and using word embeddings.
Part of
- NLP with Deep Learning — section
- Deep Learning & Neural Networks roadmap — the full learning path