# Representation & similarity

Subject: Natural Language Processing

Exam: TEE

Reader: https://deep-learning-study-room.vercel.app/nlp/#tee/formulas/f-vectors

Canonical: https://deep-learning-study-room.vercel.app/read/nlp/tee/formulas-chapter/f-vectors/

Formulas explained through full worked examples

1. [Label and one-hot encoding: three complete examples](https://deep-learning-study-room.vercel.app/read/nlp/tee/formulas/f-vectors--u2-label-onehot/)
2. [Bag of words and bag of n-grams](https://deep-learning-study-room.vercel.app/read/nlp/tee/formulas/f-vectors--u2-bow/)
3. [TF–IDF from the exact slide convention](https://deep-learning-study-room.vercel.app/read/nlp/tee/formulas/f-vectors--u2-tfidf/)
4. [Cosine: products, lengths and division](https://deep-learning-study-room.vercel.app/read/nlp/tee/formulas/f-vectors--u2-cosine/)
5. [Jaccard: intersection, union and distance](https://deep-learning-study-room.vercel.app/read/nlp/tee/formulas/f-vectors--u2-jaccard/)
6. [Manhattan, Euclidean and Minkowski: three complete comparisons](https://deep-learning-study-room.vercel.app/read/nlp/tee/formulas/f-vectors--u2-distances/)
7. [Distributional hypothesis and a co-occurrence matrix](https://deep-learning-study-room.vercel.app/read/nlp/tee/formulas/f-vectors--u5-distribution/)
8. [Word2Vec: CBOW and skip-gram from training pairs](https://deep-learning-study-room.vercel.app/read/nlp/tee/formulas/f-vectors--u5-word2vec/)
9. [Word analogies and sentence embeddings](https://deep-learning-study-room.vercel.app/read/nlp/tee/formulas/f-vectors--u5-sentence/)
10. [The GloVe objective and numerical interpretation](https://deep-learning-study-room.vercel.app/read/nlp/tee/formulas/f-vectors--u5-glove-loss/)