Now that we know about the architecture of a CNN, let's see what type of layers are used to construct it. CNNs typically use the following types of layers:
Input layer: This layer takes the raw image data as it is.
Convolutional layer: This layer computes the convolutions between the neurons and the various patches in the input. If you need a quick refresher on image convolutions, you can check out this link: http://web.pdx.edu/~jduh/courses/Archive/geog481w07/Students/Ludwig_ImageConvolution.pdf . The convolutional layer basically computes the dot product between the weights and a small patch in the output of the previous layer.
Rectified Linear Unit layer: This layer applies an activation function to the output of the previous layer. This function is usually something like max(0, x). This layer is needed to add non-linearity to the network so that it can generalize well to any type of function.
Pooling layer: This layer samples the output of the previous layer...