1. Essential Tools for Machine Learning Engineers: My Real-World Stack

    A comprehensive guide to the tools I actually use as a Machine Learning Engineer working on NLP projects. From essential development tools like PyCharm and uv, to deployment solutions like Docker and Kubernetes, to AI-powered coding assistants - this article covers the real-world toolkit that helps me ship production ML systems efficiently. Plus, honest takes on popular tools I don’t use and why.

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  2. Mathematical Model for Player Ranking

    In the previous post, we discussed how to model player skills using normal distributions. Now, let’s dive into the mathematical details of how we can estimate the parameters of these distributions using Maximum Likelihood Estimation.

    Problem definition: Having a set of players \(\mathcal{P}\) numbered from \(1\) to \(n …

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    Written by Viacheslav Zhukov in Math on
  3. Approximating Skills of Table Tennis Players Using Normal Distribution. Introduction

    The idea struck me during a friendly table tennis tournament. We had a marathon of matches to rank ourselves by skill, and somewhere in the middle of all those games, a question popped into my mind: How can we estimate players’ skill levels \(\mathcal{S}\) using just the game outcomes …

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    Written by Viacheslav Zhukov in Math on