Overview of vector embedding
Vector embeddings are one of the most interesting and useful concepts in machine learning. They are at the heart of many NLP, recommendation and search algorithms. If you've ever worked with a recommendation engine, voice assistant, language translator, etc., you've come across systems that rely on embedding.
Like most software algorithms, machine learning algorithms work with numbers. Sometimes our datasets contain numeric columns or values that can be converted to numeric (ordinal, categorical, etc.). Other times we encounter something more abstract, like an entire text document. We create vector embeddings for data like this, which are just lists of numbers, in order to perform various operations with them. Entire paragraphs of text or any other object can be reduced to a vector. Even numeric data can be converted to vectors for easy manipulation.
But there is something special about vectors ÿ