CNNs work exceptionally well on visual tasks, such as object classification and object recognition in images and videos and pattern recognition in music, sound clips, and so on. They work effectively in these areas because they are able to exploit the structure of the data to learn about it. This means that we cannot alter the properties of the data. For example, images have a fixed structure and if we were to alter this, the image would no longer make sense. This differs from ANNs, where the ordering of feature vectors does not matter. Therefore, the data for CNNs is stored in multidimensional arrays.
In computers, images are in grayscale (black and white) or are colored (RGB), and videos (RGB-D) are made of up pixels. A pixel is the smallest unit of a digitized image that can be shown on a computer and holds values in the form of [0, 255]. The...
