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 neural network deeper and useful for solving complex image processing tasks, as seen in the following diagram:

The next few sections describe each of the layers and how they work.