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Hinkmond Wong's Weblog

Adding Deep Learning AI to Internet of Things (IoT) - Part 2









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.
See:


Neural Network Perceptrons

Here's a quote:

 So how do perceptrons work? A
perceptron takes several binary
inputs, x1, x2, … and produces a
single binary output:
In the example shown the
perceptron has three inputs, x1,
x2, x3. In general it could have
more or fewer inputs.

This is the first step in adding AI to our IoT devices. In the next part we will look at how to represent a perceptron in Java. That's the easiest way, unless you want to do that in Python or JavaScript... Blech...



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