Sorry for not getting back to you sooner. I should have replied to you earlier, but our conversation seemed to fade down and I still didn’t hear what could serve as a real illustration for you.

Of course, the main background is neural networks and machine learning algorithms. As I have told you, my previous hands-on experience is protected by NDA. If you want to have a look at it, I can share it via a personal message. The company is CVisionLab («СиВижинЛаб»), you may call them for a confirmation.

This agreement is in the Russian language as it is concluded with the Russian company. To check it, it’ll be enough to contact them at the provided corporate details. If someone is really interested, bitcointalk has a significant Russian-speaking community, you may request assistance from some of the authority figures there.

For 8 months of work, a number of developments has been done at uKit. I’m afraid, there are very few areas where fast results are possible with machine learning. Speaking about something you can see in action and “touch”, here https://aesthetics-demo.ukit.space/

At the moment, https://aesthetics-demo.ukit.space/ looks more like a toy that makes it possible to evaluate your or another person’s website. But we meant it this way so that people had something to ‘touch’. Also, through gathering opinions of a wide variety of people and offering them to correct our ratings we are able to collect a bigger data set.
Basically, this is the scoring system for uKit AI and is a subsequently important, yet supplementary part of the product.

This system is built using machine learning methods. It should reflect an opinion of an average user about the website’s appearance. It’s neither about a professional designer’s opinion, nor about the website’s usability, nor about its functionality. Let me emphasize this point — it’s only about the APPEARANCE and only an AVERAGE user.
In this project, we are using one of the variants of the gradient boosting method (catboost library). To date, this approach is one of the most effective in solving problems of machine learning.

Besides, in the near future (this month), we are planning to introduce an additional evaluation by the neural network, which should improve the overall quality.

To train the model, we have gathered a sample of 12K websites. Despite the fact that at the moment the system gives reasonably good results (it is possible to play around with it on your own), it is still the first alpha version and has a number of issues:

• it can’t evaluate minimalistic websites such as google.com (this is a solvable issue, but we’d like to achieve this through machine learning rather than using “white lists”);
• it can’t work correctly with hieroglyphic websites (Chinese, Japanese…), since there were none of this kind in the existing training base;
• it has an insufficient ratings bank. We need more people and different opinions to improve the quality. It doesn’t make the system unrepresentative at the moment, but the data set needs significant enhancements, which will certainly influence the quality and robustness of the output results.

Well, I said that I can't afford it because I have to work. School of Data Analysis demands candidates should be out of any full time job. I know the person who was not allowed to pass the exams for this reason. And I mean I can't study for 2 years without earning money. If it is a reason to blame me, then I think you have to blame 90% of adults (may be less may be more).

I'm not hiding my identity in this topic. I shown up there in the team section. My name is Roman Steinberg. So, you can look at my LinkedIn, github, WebOfScience profile or whatever you want.

We both are newbies on this forum, but I don't like your style of dialogue. I tried to answer in general words. If I misunderstood your level of knowledge in the area then may be you didn't shown it first. Now you blame me that I'm hiding something. No, I just tried to answer generally. Good luck in your investments!

My previous company is outsourcing company and there were more strict rules. As far as I remember my NDA expires after 3 years from the day I quit. So, I should wait until summer 2019. I can tell you that I worked with points of interest like problem, face detection like problem, searching for anomalies, detection task. I'm using opencv, tensorflow, nvidia digits, catboost, xgboost and couple of other tools including statistics. I organized Data Science seminar for students to prepare them for Kaggle. Also I have 15 years experience in training students for programming contests, if it means something for you.

I prefer to work with interesting projects, not for famous companies. I had 3 job offers from Yandex, including autonomous driving, but I decided to work with ukit AI But two of my students were hired by Yandex couple of month ago.

Let me introduce Yana Valieva. Article about scholarship. You can see her project for Apple contest here.

She won scholarship for participating in WWDC conference. She participated successfully.

In Russia we have School of data analysis. It lasts for two years and demands from students not to work full time. I can't afford it, because I have to work , but I have an expertise in real projects. Sorry, but I can't point to them as I said.

Yes, we are qualified enough I have about 3 years of experience in the area of Data Science, Machine Learning and Computer Vision. I'm PhD in Applied Mathematics and an Associate Professor of the Mathematics, Mechanics and Computer Science Institute at the Southern Federal University. I studied cs231n and Coursera Data Science course from Moscow Institute of Physics and Technology. Projects from previous job are part of NDA and cann't be shown. Current project is ukit AI.

One more our team member successfully won Apple WWDC Scholarship due to her Machine Learning project. She is also finished Introduction to ML course from Moscow Institute of Physics and Technology about 2 years ago and applied for Andrew Ng course and cs231n.