An exhaustive and illustrated guide to Word2Vec with code!
Welcome to Part 3 of our illustrated journey through the exciting world of Natural Language Processing! If you caught Part 2, you’ll remember that we chatted about word embeddings and why they’re so cool.
Word embeddings allow us to create maps of words that capture their nuances and intricate relationships.
This article will break down the math behind building word embeddings using a technique called Word2Vec — a machine learning model specifically designed to generate meaningful word embeddings.
Word2Vec offers two methods — Skip-gram and CBOW — but we’ll focus on how the Skip-gram method works, as it’s the most widely used.
These words and concepts might sound complex right now but don’t worry — at its core, it’s just some intuitive math (and a sprinkle of machine learning magic).
Real quick — before diving into this article, I strongly encourage you to read my series on the basics of machine…