C The Univ ersit yof Amsterdam P ermission is gran ted to distribute single copies of this book for noncommercial use as long it is distributed a whole in its original form and the names of authors and Univ ersit y Amsterdam are men tioned P ermission is also gran ted to use this book for noncommercial courses pro vided the authors are notied of b.
The Convolutional Neural Network (CNN) has shown excellent performance. This note is self-contained, and the focus is to make it comprehensible to beginners in the CNN eld. 1 Introduction This is a note that describes how a Convolutional Neural Network (CNN) op-erates from a mathematical perspective. Wanttolearnnotonlyby reading,butalsobycoding? SNIPE1 is a well-documented JAVA li-brary that implements a framework for. The aim of this work is (even if it could not befulfilledatfirstgo)toclosethisgapbit by bit and to provide easy access to the subject. Paradigms of neural networks) and, nev-ertheless, written in coherent style. Neural Networks Activation Functions The most common sigmoid function used is the logistic function f(x) = 1/(1 + e-x) The calculation of derivatives are important for neural networks and the logistic function has a very nice derivative f’(x) = f(x)(1 - f(x)) Other sigmoid functions also. Filter weights are shared across receptive fields.The “dot products” between weights and inputs are “integrated” across “channels”.The process is a 2D convolution on the inputs.Convolutional neural networks are usually composed by a set of layers that can be grouped by their functionalities.
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