There are two families of algorithms that dominate the space of word embeddings. They are called Word2vec and GloVe. Both methods are responsible for producing word embeddings by learning from very large corpus (collection of text documents). The main difference between these two algorithms is that the GloVe algorithm, out of Stanford, learns word embeddings through a series of matrix statistics while Word2vec, out of Google, learns them through a deep learning approach. Both algorithms have merits and our text will focus on using the Word2vec algorithm to learn word embeddings.