As Figure 4 shows, in convolution layer, the left matrix is the input, which is a digital image, and the right matrix is a convolution matrix. The convolution layer takes the convolution of the input image with the convolution matrix and generates the output image. Usually the convolution matrix is called filter and the output image is called filter response or filter map. An example of convolution calculation is demonstrated in Figure 5 . Each time, a block of pixels is convoluted with a filter and generates a pixel in a new image.

I am sure that I have forgotten many best practices that deserve to be on this list. Similarly, there are many tasks such as parsing, information extraction, etc., which I do not know enough about to give recommendations. If you have a best practice that should be on this list, do let me know in the comments below. Please provide at least one reference and your handle for attribution. If this gets very collaborative, I might open a GitHub repository rather than collecting feedback here (I won't be able to accept PRs submitted directly to the generated HTML source of this article).

Unsupervised learning is the ability to find patterns in a stream of input. Supervised learning includes both classification and numerical regression . Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. In reinforcement learning [71] the agent is rewarded for good responses and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for operating in its problem space. These three types of learning can be analyzed in terms of decision theory , using concepts like utility . The mathematical analysis of machine learning algorithms and their performance is a branch of theoretical computer science known as computational learning theory . [72]