Neural networks – explained

As mentioned here a few times, I teach an executive course called Analytics for strategic management, as well as a short program (three days) called Decisions from Data: Driving an Organization on Analytics. We have just finished the first version of both of these courses, and it has been a very enjoyable experience. The students (in both courses) have been interested and keen to learn, bringing relevant and interesting problems to the table, and we have managed do what it said on the tin (I think) – make them better consumers of analytics, capable of having a conversation with the analytics team, employing the right vocabulary and being able to ask more intelligent questions.

Of course, programs of this type does not allow you do dive deep into how things work, though we have been able to demonstrate MySQL, Python and DataRobot, and also give the students an understanding of how rapidly these things are evolving. We have talked about deep learning, for instance, but not how it works.

But that is easy to fix – almost everything about machine learning is available on Youtube and in other web channels, once you are into a little bit of the language. For instance, to understand how deep learning works, you can check out a series of videos from Grant Sanderson, who produces very good educational videos on the web site 3 blue one brown.

(There are follow-up videos: Chapter 2, Chapter 3, and Chapter 3 (formal calculus appendix). This Youtube channel has a lot of other math-related videos, too, including a great explanation of how Bitcoin works, which I’ll have to get into at some points, since I keep being asked why I don’t invest in Bitcoin all the time.)

Of course, you have to be rather interested to dive into this, and it certainly is not required read for an executive who only wants to be able to talk intelligently to the analytics team. But it is important (and a bit reassuring) to note the mechanisms employed: Breaking a very complex problem up into smaller problems, breaking those up into even smaller problems. solving the small problems by programming, then stepping back up. For those of you with high school math: It really isn’t that complicated. Just complicated in layers.

And it is good to know that all this advanced AI stuff really is rather basic math. Just applied in an increasingly complex way, really fast.