Vector embeddings are a way to convert words and sentences and other data into numbers that capture their meaning and relationships.

Vectors represent different types as points in a multidimensional space. In this space, similar data points are clustered closer together. It is a search for similarity.

This process enables the machine to understand and process data more effectively. Vector embeddings help ML algorithms find patterns in data and perform tasks such as language translation, sentiment analysis, recommendation system and so on.

Word embeddings represent individual words as vectors, e.g. Word2vec, GloVe and Fast Text. These capture semantic relationships and contextual information from large text.

Document embeddings represent documents as vectors, e.g. newspaper articles, research papers, books. They capture semantic information and context of the whole document. There are techniques such as Doc 2 Vec and Paragraph Vectors that are designed to do document embeddings.

Sentence embeddings represent entire sentences as vectors. There are models like USE or Universal Sentence Encoder and Skip Thought to do this. These capture the meaning and context of sentences.

Image embeddings represent images as vectors. CNNs and VGG generate image embeddings.

User embeddings represent users in a system or platform as vectors. They capture preferences behaviours, and characteristics. They are used in recommendation systems and personalized marketing.

Product embeddings represent products as vectors. They capture product’s attributes, features and other semantic information of a product.