This is a continuation of story about using Data Science for finding an English teacher. If you have not read it yet - there is an opportunity to become familiar with it

Briefly  -  we had information about language teachers and tried to apply some basic ideas using pandas and our expectations. Unfortunately we got stuck on the third step, because there is not enough information for resolving our the last requirements  -  we need not more 3 candidates at the end.

Nowadays most people identify Machine Learning with training of various kinds of neural networks. At the beginning there were fully connected networks, then convolutional and recurrent networks replace them, now there exist a quite exotic variants of networks such that GAN and LTSM networks.

Their training requires constantly increasing volume of samples, and they also do not be able to explain why a particular decision was made. Structural approaches to Machine Learning avoiding these drawbacks exist, the software implementation of one of which is described in the article. This is an English translation of original post by the author.

When Alex Garland’s series Devs (on FX and Hulu) came out this year, it gave developers their own sexy Hollywood workup. Who knew that coders could get snarled into murder plots and love triangles just for designing machine learning programs? Or that their software would cause a philosophical crisis? Sure, the average day of a developer is more code writing than murder but what a thrill to author powerful new program.

Machine learning is indeed a famous word among technologies. Today we will relate it with another famous term that is Big data. Both these have become Buzz words these days. Let’s here find out their meaning individually.

Big data is known as the process in which we collect and analyze the large volume of data sets (called Big Data) which helps in discovering useful hidden patterns and other information such as customer choices, market trends which is really beneficial for the organizations to remain informed and customer-oriented business decisions.

If science were a dating app, quantum physics and machine learning probably wouldn’t be a match. They’re from completely different fields and often require completely different backgrounds and skills. But, throw in a little quantum computing and, suddenly, that science-matchmaking app becomes Tinder and the attraction between the two is palpable.

(Credit: cmo.adobe.com/articles/2017/5/how-will-artificial-intelligence-impact-business-tlp-ptr.html#gs.5zlifl)

Even though the extent of change that quantum computing will unleash on AI is up for debate, many experts now more than suspect that quantum computing will definitely alter AI at some level. Analysts from bank holding company BBVA, for example, point toward the natural synergy between quantum computing and AI as reasons why quantum machine learning will eventually best classical machine learning.

“Quantum machine learning can be more efficient than classic machine learning, at least for certain models that are intrinsically hard to learn using conventional computers,” says Samuel Fernández Lorenzo, a quantum algorithm researcher who collaborates with BBVA’s New Digital Businesses area. “We still have to find out to what extent do these models appear in practical applications.”

Hello, Habr!

About a month ago, I had a feeling of constant anxiety. I began to eat poorly, sleep even worse, and constantly read to a ton of news about the pandemic. Based on them, the coronavirus either captured, or liberated our planet, was either a conspiracy of world governments, or the vengeance of the pangolin, the virus either threatened everyone at once, or personally me and my sleeping cat…

Hundreds of articles, social media posts, youtube-telegram-instagram-tik-tok (yes, I sin) content of varying degrees of content quality did not lead me to anything but an even greater sense of anxiety.

But one day I bought buckwheat decided to end it all. As soon as possible!

Recently DeepCode, which is a static analyzer based on machine learning, began to support checking of C and C++ projects. And now we can find out the differences between the results of the classic and the machine-learning static analysis.

Static analysis is an extremely useful tool for any developer, as it helps to find in time not only errors, but also suspicious and strange code fragments that may cause bewilderment of programmers who will have to work with it in the future. This idea will be demonstrated by the analysis of the TensorFlow.NET open C# project, developed for working with the popular TensorFlow machine learning library.

ClickHouse users already know that its biggest advantage is its high-speed processing of analytical queries. But claims like this need to be confirmed with reliable performance testing. That's what we want to talk about today.



We started running tests in 2013, long before the product was available as open source. Back then, just like now, our main concern was data processing speed in Yandex.Metrica. We had been storing that data in ClickHouse since January of 2009. Part of the data had been written to a database starting in 2012, and part was converted from OLAPServer and Metrage (data structures previously used by Yandex.Metrica). For testing, we took the first subset at random from data for 1 billion pageviews. Yandex.Metrica didn't have any queries at that point, so we came up with queries that interested us, using all the possible ways to filter, aggregate, and sort the data.

ClickHouse performance was compared with similar systems like Vertica and MonetDB. To avoid bias, testing was performed by an employee who hadn't participated in ClickHouse development, and special cases in the code were not optimized until all the results were obtained. We used the same approach to get a data set for functional testing.

After ClickHouse was released as open source in 2016, people began questioning these tests.

Machine learning has firmly entrenched in a variety of human fields, from speech recognition to medical diagnosing. The popularity of this approach is so great that people try to use it wherever they can. Some attempts to replace classical approaches with neural networks turn up unsuccessful. This time we'll consider machine learning in terms of creating effective static code analyzers for finding bugs and potential vulnerabilities.

The beginning

It all started when I found an app. on Apple market, that supposedly was able to determine the ripeness of a water mellon. A program was… strange. Just think about it: instead of knocking using your knuckles, you were supposed to hit the water mellon with your iPhone! Nevertheless, I have decided to repeate that functionality on an Andtoid platform.

Author: Sergey Lukyanchikov, Sales Engineer at InterSystems

Fixing the terminology

A robot is not expected to be either huge or humanoid, or even material (in disagreement with Wikipedia, although the latter softens the initial definition in one paragraph and admits virtual form of a robot). A robot is an automate, from an algorithmic viewpoint, an automate for autonomous (algorithmic) execution of concrete tasks. A light detector that triggers street lights at night is a robot. An email software separating e-mails into “external” and “internal” is also a robot.

Artificial intelligence (in an applied and narrow sense, Wikipedia interpreting it differently again) is algorithms for extracting dependencies from data. It will not execute any tasks on its own, for that one would need to implement it as concrete analytic processes (input data, plus models, plus output data, plus process control). The analytic process acting as an “artificial intelligence carrier” can be launched by a human or by a robot. It can be stopped by either of the two as well. And managed by any of them too.

Artificial Intelligence refers to the theory of computer systems or human-made robots programmed with performing tasks as humans, such as learning, generalization, and reasoning. With this ability, AI has become a significant part of human lives. Similarly, AI and the education & tutoring web solutions are inseparable from being observed by the astounding inventions enabling machines to mimic human roles.

Visual Studio IntelliCode uses machine learning to offer useful, contextually-rich code completion suggestions as you type, allowing you to learn APIs more quickly and code faster. Although IntelliCode’s base model was trained on over 3000 top open source C# GitHub repositories, it does not include all the custom types in your code base. To produce useful, high-fidelity, contextually-rich suggestions, the model needs to be tailored to unique types or domain-specific APIs that aren’t used in open source code. To make IntelliSense recommendations based on the wisdom of your team’s codebase, the model needs to train with your team’s code.

Earlier this year, we extended our ML model training capabilities beyond our initial Github trained base model to enable you to personalize your IntelliCode completion suggestions by creating team models trained on your own code.

The artificial intelligence global market is expected to reach $190 billion by 2025. The bright future of this technology allures every entrepreneur. In fact, when we think about the technologies that are going to rule in the future, the one name that comes to our minds is ~ Artificial intelligence.

AI along with its subsets like machine learning and deep learning is making such things possible which were unimaginable by humankind a few years back. It is affecting the realities and sometimes changing reality completely.



The power of AI is well acknowledged by businesses as 84% of respondents in a study voted that they believe artificial intelligence will allow them to enjoy a competitive edge over competitors.

Although entrepreneurs have an idea about AI but what most of them lack is proper implementation. The use of optimum programming tools for a complex technology like AI can create wonders for the world of business.

Every custom web developer knows that a python is an apt tool for building AI-enabled -applications. The language has been used to create 126,424 websites so far. Since its launch in the late 1980s, python has seen remarkable growth not only in users but in applications too.

Python is the favorite language for software developers to create applications that have artificial intelligence, machine learning, etc features embedded in them. But there are reasons behind everything.

This blog is written with the intent to unveil these reasons. Let’s explore why python is extensively used in AI-enabled software development services.

“Instead of using technology to automate processes, think about using technology to enhance human interaction.” ~ Tony Zambito, Lead authority in Buyer Personas.

Do you know ~ according to research, 93% of customers make purchase decisions based on visual appearance. Visual elements of your brand are the key deciding factors for a majority of potential customers.

Your logo, website colors, chatbot texts, etc all have an impact on the psychology of people who come across them. Some colors or features attract them and some make them leave your website instantly.

In this era, interactive features with the help of technologies like Artificial intelligence are enhancing such effects. AI has the power to add interactive elements to your presentation. This creates a connection between your company and its customers.

ML.NET is a cross-platform, machine learning framework for .NET developers, and Model Builder is the UI tooling in Visual Studio that uses Automated Machine Learning (AutoML) to easily allow you to train and consume custom ML.NET models. With ML.NET and Model Builder, you can create custom machine learning models for scenarios like sentiment analysis, price prediction, and more without any machine learning experience!

ML.NET Model Builder

This release of Model Builder comes with bug fixes and two exciting new features:

• Image classification scenario – locally train image classification models with your own images
• Try your model – make predictions on sample input data right in the UI

Photo by Dugan Arnett on Boston Globe

Are you still looking for a new flat? Ready to make the last attempt? If so  -  follow me and I show you how to reach the finish line.

A palette makes it easy for painters to arrange and mix paints of different colors as they create art on the canvas before them. Having a similar tool that could allow AI to jointly learn from diverse data sources such as those for conversations, narratives, images, and knowledge could open doors for researchers and scientists to develop AI systems capable of more general intelligence.


A palette allows a painter to arrange and mix paints of different colors. SpaceFusion seeks to help AI scientists do similar things for different models trained on different datasets.

You’re utterly focused. You’ve lost track of time. Nothing else in the world exists. You’re living in the moment.

While this might sound like meditation, it’s a description that can also be applied to the state of flow – the feeling of being so engaged by your work, that you lose yourself to it completely, while massively increasing your productivity in the process.

It’s the holy grail that we all strive for, whether it’s a hobby we’re passionate about, or a project at work. Achieving our best and utilising our maximum potential at all times, can however, be a struggle.
We had the pleasure of talking with Dr. Jack Lewis, a neuroscientist with a passion for exploring how our minds work, to see what motivates us to do our best work, and the important roles that workplace environments, culture, and technology can play.