| 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.
|
