Taking as an example the input matrix 5x5 as shown earlier, a CNN consists of an input layer consisting of 25 neurons (5x5 = 25) whose task is to acquire the input value corresponding to each pixel and transfer it to the next hidden layer.
In a multilayer network, the outputs of all neurons of the input layer would be connected to each neuron of the hidden layer (fully-connected layer).
In CNN networks, the connection scheme that defines the convolutional layer that we are going to describe is significantly different.
As you can probably guess, this is the main type of layer; the use of one or more of these layers in a CNN is indispensable.
In a convolutional layer, each neuron is connected to a certain region of the input area called the receptive field.
For example, using a 3x3 kernel filter, each neuron will have a bias and 9=3x3 weights connected to a single receptive field. Of course, to effectively...