Convolutional layers and filters are at the heart of convolutional neural networks. In these layers, we slide a filter (also referred to in this text as a window or kernel) over our ndarray feature and take the inner product at each step. Convolving our ndarray and kernel in this way results in a lower-dimensional image representation. Let's explore how this works on this grayscale image (available in image-assets repository):
The preceding image is a 5 x 5 pixel grayscale image shows a black diagonal line against a white background.
Extracting the features from the following diagram, we get the following matrix ...
