Embedding model

Topic

An embedding model is a machine learning model that implements representation learning techniques to map complex, high-dimensional data into a lower-dimensional vector space. These models represent inputs such as words, sentences, images, or graphs as dense numerical vectors, capturing their semantic meaning and relationships. They are widely used in natural language processing, computer vision, and recommendation systems to enable efficient similarity searches and downstream machine learning tasks.