# N-grams and language models

Subject: Natural Language Processing

Exam: M2

Reader: https://deep-learning-study-room.vercel.app/nlp/#m2/read/m2-language-models

Canonical: https://deep-learning-study-room.vercel.app/read/nlp/m2/read-chapter/m2-language-models/

Unit 3, pages 62–108; essential probability revision

1. [Words, counts and probabilities: the notation you need](https://deep-learning-study-room.vercel.app/read/nlp/m2/read/m2-language-models--words-counts-probability/)
2. [Unigrams, bigrams and trigrams: construct them correctly](https://deep-learning-study-room.vercel.app/read/nlp/m2/read/m2-language-models--n-grams-construction/)
3. [Collocations and the purpose of a language model](https://deep-learning-study-room.vercel.app/read/nlp/m2/read/m2-language-models--collocations-and-lm/)
4. [Probability chain rule and the Markov assumption](https://deep-learning-study-room.vercel.app/read/nlp/m2/read/m2-language-models--chain-rule-markov/)