Natural Language Processing (NLP)

Word Embeddings

We represent words as vectors.

This example each word is represent by a vector of 5 numbers.

Vectors normally range from sizes of 50-200 numbers. This allows words to have more meaning and relationships between other words

Some pre-trained embbedings are Word2Vec and Glove

Language modelling

a model that can predict the next word in a sentence given a few words of a sentence

torch text is pytroches nlp

Tokenization breaks words into tokens.