The main building block of CNN is the convolutional layer. The convolutional layer consists of a bunch of convolution filters (kernels), which we already discussed in detail in Chapter 2, Sampling, Fourier Transform, and Convolution. The convolution is applied on the input image using a convolution filter to produce a feature map. On the left side is the input to the convolutional layer; for example, the input image. On the right is the convolution filter, also called the kernel. As usual, the convolution operation is performed by sliding this filter over the input. At every location, the sum of element-wise matrix multiplication goes into the feature map. A convolutional layer is represented by its width, height (the size of a filter is width x height), and depth (number of filters). Stride specifies how much the convolution filter will be moved at each step (the default value is 1). Padding refers to the layers of zeros to surround the input (generally used to keep the input and output image size the same, also known as same padding). The following screenshot shows how 3 x 3 x 3 convolution filters are applied on an RGB image, the first with valid padding and the second with the computation with two such filters with the size of the stride=padding=1: