The Illustrated Word2vec - A Gentle Intro to Word Embeddings in Machine Learning
The concept of word embeddings is a central one in language processing (NLP). It's a method of representing words as numerically -- as lists of numbers that capture their meaning. Word2vec is an algorithm (a couple of algorithms, actually) of creating word vectors which helped popularize this concept. In this video, Jay take you in a guided tour of The Illustrated Word2Vec, an article explaining the method and how it came to be developed.
The article: https://jalammar.github.io/illustrate...
The talk: • Intuition & Use-Cases of Embeddings i...
Word2vec paper: https://proceedings.neurips.cc/paper/...
By Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg Corrado, and Jeffrey Dean
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