I recently started an AI-focused educational newsletter, that already has over 90,000 subscribers. TheSequence is a no-BS (meaning no hype, no news etc) ML-oriented newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Word embeddings is one of the most established techniques in the world of natural language processing(NLP). Conceptually, word embeddings are language modeling methods that map phrases or words in a sentence to vectors and numbers.

Word embeddings have revolutionized the world of natural language processing(NLP). Conceptually, word embeddings are language modeling methods that map phrases or words in a sentence to vectors and numbers. One of the first steps in any NLP application is to determine what type of word embedding algorithm is going to be used. Typically, NLP models resort to pretrained word embedding algorithm such as Word2Vec, Glove or FastText. While that approach is relatively simple, it also results highly inefficient as is near to impossible to determine what word embedding will perform better as the NLP model evolves.