Rank-N Tensors

A rank-N tensor is a mathematical object that generalizes vectors (rank-1) and matrices (rank-2) to higher dimensions. Think of it as a multi-dimensional array that can handle complex data structures. For example, a rank-1 tensor is a list of numbers, a rank-2 tensor is a grid or table, and a rank-3 tensor is like a cube of data. Higher ranks involve more dimensions, useful in advanced fields like physics, machine learning, and data analysis to describe relationships in multi-way data. The "rank" indicates the number of dimensions or ways data can vary independently.