• Agile English teaching. What is it?



      Modern-day agile English teaching has come to take the place of rigid, cut-and-dried lessons that are fast becoming a thing of the past.

      Let me clarify what I mean by agile teaching that is bound to substitute conventional teaching.

      Some decades ago and up until recently it was perfectly valid to choose a certain textbook and go through it module by module together with your students (be it a group or individual learners). Given the abundance of high-quality materials readily accessible online and offline, it is completely unthinkable to proceed with this outdated approach.
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    • TOP-23 Language Learning Apps

        There are hundreds of language learning apps and hundreds of reviews and comparisons. Actually most of the comparisons are about the same programs. Are the apps really helpful or this is only ad and marketing? Yes, and Yes.

        I have been studying English using various methods and resources over five years. Language learning is not my greatest talent but I have achieved B2 level (from A2) using only my smartphone and PC. I found a set of features that really helps you study a foreign language. Some of them are crucial, others are just useful. Under the cut you will find a rating of the language learning apps that I composed by analyzing these features, As Objective As Possible.
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      • Python Vs R — Data Science

        • Tutorial
        When mulling over the best programming language to use for data science, Python and R ring a bell (very quickly). While there are a lot of languages like C, C++, Java, Julia, Perl, and Scala, it's protected to state that Python and R are the harbingers in data science.

        While a great deal of data researchers will discuss the customary shortcomings like data wrangling in R or data representation in Python, ongoing improvements like Altair for Python or R have adequately reacted to these shortcomings.

        So which one would it be a good idea for you to decide for your next data investigation venture?

        R has been ruling this space for a long time now. This bodes well as this programming language was explicitly intended for analysts.
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      • C# or Java? TypeScript or JavaScript? Machine learning based classification of programming languages

          GitHub hosts over 300 programming languages—from commonly used languages such as Python, Java, and Javascript to esoteric languages such as Befunge, only known to very small communities.


          Figure 1: Top 10 programming languages hosted by GitHub by repository count 

          One of the necessary challenges that GitHub faces is to be able to recognize these different languages. When some code is pushed to a repository, it’s important to recognize the type of code that was added for the purposes of search, security vulnerability alerting, and syntax highlighting—and to show the repository’s content distribution to users.

          Linguist is the tool we currently use to detect coding languages at GitHub. Linguist a Ruby-based application that uses various strategies for language detection, leveraging naming conventions and file extensions and also taking into account Vim or Emacs modelines, as well as the content at the top of the file (shebang). Linguist handles language disambiguation via heuristics and, failing that, via a Naive Bayes classifier trained on a small sample of data. 

          Although Linguist does a good job making file-level language predictions (84% accuracy), its performance declines considerably when files use unexpected naming conventions and, crucially, when a file extension is not provided. This renders Linguist unsuitable for content such as GitHub Gists or code snippets within README’s, issues, and pull requests.

          In order to make language detection more robust and maintainable in the long run, we developed a machine learning classifier named OctoLingua based on an Artificial Neural Network (ANN) architecture which can handle language predictions in tricky scenarios. The current version of the model is able to make predictions for the top 50 languages hosted by GitHub and surpasses Linguist in accuracy and performance.
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        • AdBlock has stolen the banner, but banners are not teeth — they will be back

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        • How to learn English

          • Tutorial

          One one hand I don't want to be the final authority, but on the other hand, I'd like to share my point of view on how to learn English. The English language is not secret knowledge; it is just a lot of hard training. One of the most important bullets is constantly improving English. You should do it from day to day if you want to approach result. It must not loathe torture for you, It means that you should find out something interesting in that process.

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