In this monograph we research further and in depth the more intrinsic and sophisticated quantitative approximation properties of Overactivated Neural Network Operators and Overactivated Convolution Operators converging to the unit operator. The employed activation functions are seven well known activation functions, having all the properties of tanhx, leading to cusp composite activation functions of compact support, or multi-composite activation functions of infinite domain. The number of produced neural network operators can be finite or infinite depending on our domains being used as finite or infinite.  We treat also, similarly, the generalized Symmetrization case. The goal is to achieve supersonic speeds of convergence. We derive univariate and multivariate results for ordinary, fractional and fuzzy approximations to the unit operator. The presented approaches are original, and chapters are self-contained and can be read independently. 
This treatise results are expected to find applications in Applied Mathematics, Computer Science, Engineering, Economics, AI, Machine Learning and Deep Learning. Therefore this monograph is suitable for researchers, graduate students, practitioners and seminars of the above disciplines, also to be in all Science and Engineering libraries.