Dot Product

The dot product is a fundamental operation obtained by multiplying corresponding components of two vectors and summing the results. Geometrically, it helps measure how aligned two vectors are. In machine learning, linear models, similarity calculations, attention mechanisms, and embedding-based systems rely heavily on the dot product. Metrics such as cosine similarity are built on this logic. Therefore, the dot product is an essential tool for both algebraic interaction and semantic closeness.