Written by Sergio Sancho Azcoitia, Security Researcher for ElevenPaths
Last month, we started a two-article series talking about Lisp. Part I covered the history, its beginnings and uses when it comes to creating neural networks. Today however, we will show you how Lisp works, and how you can create a simple neural network.
Our neural network in question will be simple, meaning it will only consist of three layers, and its function will be to find whom we are thinking about, out of all our team members. If we see the image below, the nodes or neurons correspond to the names of our teammates and the layers correspond to the conditions that lead us to one answer or another.
|Figure 2. Even though this neural network consists of 3 layers, you can always increase the complexity of a neural network by adding more layers and sublayers (Source: Gengiskanhg, Wikipedia Spain)
|Figure 3. Our example of a neural network that tells us who are thinking about|
As we have said before, this example is simple, but starting with this as a base, you care create neural networks that are much more complex, adding layers and sublayers. A clear example of this is expert system Akinator, that we have mentioned before on the blog. In the case of Akinator, it counts with an immense neural network that increases as new characters (nodes) are added to the game.