Self Organizing Maps (SOM’s) – Plan of attack

Before we delve into this section on self-organizing maps (SOMs), let’s go through the section’s plan of attack. Here’s what you will get to learn in this section:

• How self-organizing maps work

As with artificial neural networks, this part of the SOM section will give you a better understanding of what the sections aims at. This will happen as we examine together how the SOMs themselves learn.

• K-Means Clustering (Why) (Part 1) (Part 2) (Part 3)

We’ll have a recap of the process of K-means clustering which you have supposedly passed by during our machine learning course. Understanding K-means clustering will give you a more vivid comprehension of SOMs.

• How do self-organizing maps learn? (Part 1) (Part 2)

This question will be answered over two parts of this section. That’s not because of its complexity. Self-organizing maps are actually one of the simplest topics that we’ll cover in our course. It’s quite an interesting topic, though, and the material available for it is pretty rich.

• Live SOM Example

Although the example we’ll be having in this section is very straightforward, it will reveal to you a lot about SOMs; how they work, structure themselves, learn, capture similarities and correlations in your data sets, and portrays them in a two-dimensional map.

Basically, it will wrap up everything that we’ll be covering in this section.

• Reading an Advanced SOM

That’s the final part in the SOM section. We’ll have multiple SOMs on the screen in front of us, and you’ll learn how to read these maps, as well as various implementations of SOMs that will enable you to dig deeper into the topic if you happen to find it intriguing.

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