Estimation of deep neural networks capabilities based on a trigonometric approach
Paweł Różycki, Janusz Kolbusz, Bogdan M. Wilamowski · 2016
The rapid development of computing machines led to renewed interest in deep neural networks. For years it is known that they have a great possibilities, but to use them new training algorithms are required. The paper shows benefits for deep neural networks usage by analysis of the Fourier series approximation of the activation function for shallow and deep neural network architectures. The proposed approach has been confirmed by experiments.