• Machine Learning for your flat hunt. Part 2

      
      Have you thought about the influence of the nearest metro to the price of your flat? 
      What about several kindergartens around your apartment? Are you ready to plunge in the world of geo-spatial data?


      The world provides so much information…
      
      

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    • Contextual Emotion Detection in Textual Conversations Using Neural Networks


        Nowadays, talking to conversational agents is becoming a daily routine, and it is crucial for dialogue systems to generate responses as human-like as possible. As one of the main aspects, primary attention should be given to providing emotionally aware responses to users. In this article, we are going to describe the recurrent neural network architecture for emotion detection in textual conversations, that participated in SemEval-2019 Task 3 “EmoContext”, that is, an annual workshop on semantic evaluation. The task objective is to classify emotion (i.e. happy, sad, angry, and others) in a 3-turn conversational data set.
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      • How do you choose products in stores?

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        The most important single ingredient in the formula of success is knowing how to get along with people. Theodore Roosevelt

        In the previous article I tried to cover the basics of pricing analytics. Now I'd like to talk about something more interesting.

        Have you ever thought about why you choose certain products in stores, why you prefer them to other similar ones? Many shopping trips are spontaneous, so it's probably impossible to give a clear answer for all the times you go shopping. But the general idea is obvious: you go shopping for a specific reason (to get food, a gadget, for entertainment, to play blackjack). In this article I'm going to use available data from grocery retailers to talk about how a set of basic logical assumptions and community analysis can help us determine the way customers choose products.
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      • A selection of Datasets for Machine learning

          Hi guys,

          Before you is an article guide to open data sets for machine learning. In it, I, for a start, will collect a selection of interesting and fresh (relatively) datasets. And as a bonus, at the end of the article, I will attach useful links on independent search of datasets.

          Less words, more data.

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          A selection of datasets for machine learning:


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        • .NET, TensorFlow, and the windmills of Kaggle — the journey begins

          This is a series of articles about my ongoing journey into the dark forest of Kaggle competitions as a .NET developer.

          I will be focusing on (almost) pure neural networks in this and the following articles. It means, that most of the boring parts of the dataset preparation, like filling out missing values, feature selection, outliers analysis, etc. will be intentionally skipped.

          The tech stack will be C# + TensorFlow tf.keras API. As of today it will also require Windows. Larger models in the future articles may need a suitable GPU for their training time to remain sane.
          Let's predict real estate prices!
        • AdBlock has stolen the banner, but banners are not teeth — they will be back

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