**What is Banach space?**

A Banach space is a completely normalized vector space in mathematical analysis. That is, the distance between vectors converges closer to each other as the sequence advances. The term is named after the Polish mathematician Stefan Banach (1892-1945), who is considered one of the founders of functional analysis.

In computer science, the mathematician Shahar Mendelson used the Banach space in machine learning to limit the errors of machine learning algorithms.

In functional analysis, a Banach space is a normalized vector space that calculates the vector length. If the vector space is normalized, it means that every vector other than the zero vector has a length that is greater than zero. The length and the distance between two vectors can thus be calculated. The vector space is complete, which means that a Cauchy sequence of vectors in a Banach space will converge towards a boundary. As the sequence advances, the distances between vectors arbitrarily come closer together.

Banach spaces are widely used in functional analysis, with other spaces in the analysis being Banach spaces. In computer science, Banach spaces have also been applied to machine learning algorithms to measure the generalization error, or how accurate a machine learning algorithm is. In particular, the mathematician Shahar Mendelson used the Banach spaces to improve the reliability of machine learning algorithms.