Vector Embeddings Techniques

Vector embeddings - A Techniques which efficiently convert categorical data into a numerical format, capturing semantic meanings and help us to find meaningful close related data.

Well known techniques such as Word2Vec, GloVe, and BERT. 

Here is the paper published by University of Colorado Boulder which well defined the techniques and how it works - http://wordvec.colorado.edu/word_embeddings.html

Statistics and Mathematics are the platform where from this innovation evolved.

We used vector embeddings in real-world applications like chatbots, sentiment analysis, translation services, recommendation text and so on.

Needless to say it has challenges as well. Challenges like parsing, data ingestion, diverse data formats, preprocessing, embedding generation we face while deal with and overcome with this.

In very simple way, each word in your documnet (content/image/audio) transform into numerical value and stored like an array which help query/AI search to find the neareset/closet matches.

Document >> Embedding Transoformation >> [......Numerical Values.......] >> Help to find nearest neighbour

We have various method to deal with word embeddings, sentence embeddings,  graph embeddings, audio embeddings. 

To know more, search OpenAI’s approach to handling different embedding techniques in ChatGpt.com