11. A single-layer neural network computes a continuous output instead of a step function . 12. This is a random step function . 13. For having a continuous antiderivative, one has thus to add a well chosen step function . 14. This distribution appears, for example, in the Fourier transform of the Heaviside step function . 15. Where \ delta is the Dirac delta function and H ( x ) the Heaviside step function . 16. That is, the dipole density includes a Heaviside step function locating the dipoles inside the surface. 17. Extends to a linear map on the vector space of step functions on " X ". 18. This cumulative distribution function is a step function that jumps up by at each of the data points. 19. The theory of distributions clarifies the ( then ) mysteries of the Dirac delta function and Heaviside step function . 20. Rather a computer is a symbol manipulator that follows step by step functions to compute input and form output.
