This document discusses word embeddings, focusing on techniques like one-hot encoding, Word2Vec, and GloVe for representing words as vectors in natural language processing. It highlights the limitations of one-hot encoding and the advantages of vector representations for capturing semantic and syntactic relationships between words. Additionally, it outlines the continuous bag of words (CBOW) and skip-gram models for training word embeddings and touches upon the computational complexities involved.