Go

This summer I spent quite a bit of time speaking to various people about “intelligent” Edge computing. I put double quotes around the word intelligent to avoid the wrath of the thought leaders on the internet as they fight each other over what intelligence is and what is not.

The more I talked to people the more I was realizing the possibilities and opportunities the edge computing opens up for the future: “injecting” intelligence into dumb physical objects seems akin to injecting “life” to dead objects, at least for us, scifi fans, which makes up for interesting vision of the future (for brevity let’s ignore the IoT security issues in this post). The possibilities are even more exciting in light of machine learning and neural networks models getting lighter and more easily deployable in the physical space. And let’s not forget about all the efforts to bring intelligence into some variation of IoT blockchain.

As I continue to explore the realm of Artificial Neural Networks (ANN) I keep learning about some really cool types of neural networks which don’t get much attention these days. Many of these networks have not found much practical use in the “real world” for various reasons. Nevertheless, I find learning about them in my free time somewhat stimulating as they often make me think of some problems from a different perspective. One such ANN is called Hopfield Network (a.k.a. Hopfield net), named after its inventor John Hopfield.

Couple of months ago I came across a type of Artificial Neural Network I knew very little about: Self-organizing map (SOM). I vaguely remembered the term from my university studies. We scratched upon it when we were learning about data clustering algorithms. So when I re-discovered it again, my knowledge of it was very basic, almost non-existent. It felt like a great opportunity to learn something new and interesting, so I rolled up my sleeves, dived into reading and hacking.

My rekindled interest in Machine Learning turned my attention to Neural Networks or more precisely Artificial Neural Networks (ANN). I started tinkering with ANN by building simple prototypes in R. However, my basic knowledge of the topic only got me so far. I struggled to understand why certain parameters work better than others. I wanted to understand the inner workings of ANN learning better. So I built a long list of questions and started looking for answers.

2014-07-30 22:35 Update: I’ve updated the post with the link to netlink RFC. I’ve also replaced references to golang with Go programming language on majority of mentions in the article. I do agree with the people in discussions on the topic of Go/golang, but I’ve adpoted golang in my vocabulary as that’s my standard search term on Google for the information about Go language, hence the abundance of the word golang in the original post.