Word Representations: Matrix Factorization

This lecture covers the process of finding numerical representations for words using matrix factorization, focusing on text data. It explains the importance of capturing word semantics and constructing good feature representations, benefiting various machine learning applications. The lecture also delves into the co-occurrence matrix, which represents word associations in a corpus of text, and the GloVe model, a variant of word2vec. Additionally, it discusses the Skip-Gram model for learning word representations and the training techniques involved in the process.