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Many applications, such as system identification, classification of time series, direct and inverse problems in partial differential equations, and uncertainty quantification lead to the question of approximation of a non-linear operator between metric spaces X and Y. We study the problem of determining the degree of approximation of such operators on a compact subset K X ⊂ X using a finite amount of information. If F : K X → K Y , a well established strategy to approximate F (F) for some F ∈ K X is to encode F (respectively, F (F)) in terms of a finite number d (respectively m) of real numbers. Together with appropriate reconstruction algorithms (decoders), the problem reduces to the approximation of m functions on a compact subset of a high dimensional Euclidean space R d , equivalently, the unit sphere S d embedded in R d + 1. The problem is challenging because d , m , as well as the complexity of the approximation on S d are all large, and it is necessary to estimate the accuracy keeping track of the inter-dependence of all the approximations involved. In this paper, we establish constructive methods to do this efficiently; i.e., with the constants involved in the estimates on the approximation on S d being O (d 1 / 6). We study different smoothness classes for the operators, and also propose a method for approximation of F (F) using only information in a small neighborhood of F , resulting in an effective reduction in the number of parameters involved. To further mitigate the problem of large number of parameters, we propose prefabricated networks, resulting in a substantially smaller number of effective parameters. The problem is studied in both deterministic and probabilistic settings. [ABSTRACT FROM AUTHOR]