keon/awesome-nlp

Text embeddings allow deep learning to be effective on smaller datasets. These are often first inputs to a deep learning archiectures and most popular way of transfer learning in NLP. Embeddings are simply vectors or a more generically, real valued representations of strings. Word embeddings are considered a great starting point for most deep NLP tasks. The most popular names in word embeddings are word2vec by Google (Mikolov) and GloVe by Stanford (Pennington, Socher and Manning).