Neural network in machine learning has interesting similarity with quiver representation theory. In this talk, I will build an algebro-geometric formulation of a computing machine, which is well-defined over the moduli space of representations. The main algebraic ingredient is to extend noncommutative geometry of Connes, Cuntz-Quillen, Ginzburg to near-rings, which capture the non-linear activation functions in neural network. I will also explain a uniformization between spherical, Euclidean and hyperbolic moduli of framed quiver representations.