CNNs in action
Taking as an example the 5×5 input matrix shown earlier, a CNN is made up of an input layer consisting of 25 neurons (5×5) that has the task of acquiring the input value corresponding to each pixel and transferring it to the next layer.
In a multilayer network, the output from all of the neurons in the input layer would be connected to each neuron in the hidden layer (the fully connected layer). In CNN networks, however, the connection scheme that defines the convolutional layer that we are going to describe is significantly different. As you may be able to 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 3×3 kernel filter, each neuron will have a bias and 9 weights (3×3) connected to a single receptive field. To effectively recognize an image, we need various different kernel filters...
