The first step in adding Deep Learning AI to the Internet of Things, is to understand what a perceptron is. A perceptron is an artificial neuron, the same that you find in most people's brains (most people...), but written in software like in the Java programming language.
In the diagram you can see how the perceptron takes inputs (like sensor inputs from a motion sensor or light sensor on an IoT network) represented by x1, x2, x3, etc. then runs them through some software and to get an output, such as "dog", "cat", "terrorist", "policman", etc. You can see how powerful it would be to hook up all the IoT sensors there are out there and have Deep Learning AI be able to identify objects and targets quickly using Java algorithms or programs.
This article (linked below) tells more about what perceptrons are and why they are important to Deep Learning. Above you see the perceptron written out algebraically which gives us insight on how it should be programmed in Java.
Here's a quote:
So how do perceptrons work? A