6 types of vector embeddings for your AI applications.
When we’re talking about vector embeddings, mostly we’re referring to dense vector embeddings.
Instead of engineering vector embeddings, we often train models to translate objects to vectors. A deep neural network is a common tool for training such models. The resulting embeddings are usually high dimensional (up to two thousand dimensions) and dense (all values are non-zero). For text data, models such as Word2Vec, GLoVE, and BERT transform words, sentences, or paragraphs into vector embeddings.