What is embedding?

Embedding, in the context of machine learning and data science, refers to the process of mapping discrete variables (e.g., words, categories, items) to vectors of real numbers. These vectors represent the original variables in a lower-dimensional space while preserving semantic relationships and contextual information. Embeddings are commonly used for tasks like natural language processing (word embeddings, sentence embeddings), recommendation systems (item embeddings, user embeddings), and graph analysis (node embeddings).