CNNs are deep neural networks for which the primarily used input is images. CNNs learn the filters (features) that are hand-engineered in traditional algorithms. This independence from prior knowledge and human effort in feature design is a major advantage. They also reduce the number of parameters to be learned with their shared-weights architecture and possess translation invariance characteristics. In the next subsection, we'll discuss the general architecture of a CNN and how it works.
The next screenshot shows the typical architecture of a CNN. It consists of one or more convolutional layer, followed by a nonlinear ReLU activation layer, a pooling layer, and, finally, one (or more) fully connected (FC) layer, followed by an FC softmax layer, for example, in the case of a CNN designed to solve an image classification problem.
There can be multiple convolution ReLU pooling sequences of layers in the network, making the...
