Machine learning *

A few years ago I had a feeling that Oleg Chumakov (then working at the game studio Nival) was the most famous programmer in the game development industry. He was giving speeches, hosted Gamesjams and frequently showed up on the podcast How games are made.

When VR hit the market, Oleg was chosen to lead the company’s new department — NivalVR. But, as you probably know, VR didn’t quite take off as much as people expected.

I kind of moved to other to other things in life and stopped keeping up with game development for a while, but after getting into it again I noticed that things were looking up for Oleg’s team. Now it’s called Luden.io, and their machine learning expert simulator, while True: learn() became a huge hit in its admittedly small niche. Lots of cool stories are happening around the game and the team.

We decided to do an interview with Oleg, but I couldn’t stick to one topic — his life up to this moment has been, for the lack of a better word, “interesting”. He’s seen it all. And, to ensure that a programmer could talk about programming without fear of looking too “nerdy”, the interview was conducted by my friend, colleague and an experienced developer of its own fillpackart.

In 2017, the media heard a very interesting story about a startup that robotizes milking cows on industrial dairy farms. The company is called R-SEPT, and back then it received 10 million rubles of investment. But a year has passed, and there's still no news on what happened further. We contacted Aleksey Khakhunov (AlexeiHahunov), the founder of the startup, and discussed the development. It turns out that the whole year his team was getting the prototype of the robot into shape, and just a week ago they conducted their first field test on the farm.

Under the cut there's a story about a robotics student who grew up on his parents' farm, turned the University diploma into an industrial startup, as he collected the first manipulators with his friends, and then scaled up to the level of state programs for the robotization of agriculture. And the most important is how the iron hand of the robot and the machine vision are better than a living milkmaid.

When you study an abstract subject like linear algebra, you may wonder: why do you need all these vectors and matrices? How are you going to apply all this inversions, transpositions, eigenvector and eigenvalues for practical purposes?

Well, if you study linear algebra with the purpose of doing machine learning, this is the answer for you.

In brief, you can use linear algebra for machine learning on 3 different levels:

• application of a model to data;
• training the model;
• understanding how it works or why it does not work.