Introduction
In mathematics, a convolution is a function which is applied over the output of another function. In our case, we will consider applying a matrix multiplication (filter) across an image. See the following diagram for a conceptual understanding of how image convolution can work:
Figure 1: How a convolutional filter applied across an image (length by width by depth) operates to create a new feature layer. Here we have a 2x2 convolutional filter, operating in the valid spaces of the 5x5 input with stride 1 in both directions. The result is a 4x4 matrix.
Convolutional neural networks also have other operations that fulfill other necessities, such as introducing non-linearities (ReLU), or aggregating parameters (maxpool), and other similar operations.The preceding image is an example of applying a convolution operation on a 5x5 array with the convolutional filter being a 2x2 matrix. The step size is 1 and we only consider valid placements. The trainable variables in this operation...
