Basic Overview of Convolutional Neural Network (CNN)
The Principle of the Convolutional Layer, Activating Function, Pooling Layer and Fully-connected Layer
Convolutional Neural Network is a class of deep neural network that is used for Computer Vision or analyzing visual imagery.
Convolutional Layer
Computers read images as pixels and it is expressed as a matrix (NxNx3) — (height by width by depth). Images make use of three channels (RGB), so that is why we have a depth of 3.
The Convolutional Layer makes use of a set of learnable filters. A filter is used to detect the presence of specific features or patterns present in the original image (input). It is usually expressed as a matrix (MxMx3), with a smaller dimension but the same depth as the input file.
This filter is convolved (slided) across the width and height of the input file, and a dot product is computed to give an activation map.
Different filters which detect different features are convolved on the input file and a set of activation maps is outputted which is passed to the next layer in the CNN.
