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Data Science Weekly Newsletter
July 13, 2017

Editor's Picks

  • Technical Debt in Machine Learning
    Experienced teams know when to back up seeing a piling debt, but technical debt in machine learning piles extremely fast. You can create months worth of debt in a matter of one working day and even the most experienced teams can miss a moment when the debt is so huge that it sets them back for half a year, which is often enough to kill a fast-pacing project...

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Data Science Articles & Videos

  • Are Search Engines Fair? Auditing Search Engines for Differential Satisfaction
    Many online services, such as search engines, social media platforms, and digital marketplaces, are advertised as being available to any user, regardless of their age, gender, or other demographic factors. However, there are growing concerns that these services may systematically underserve some groups of users...
  • Where Machine Learning meets rule-based verification
    This post addresses some high-level questions like: Longer term, how much of the verification of Intelligent Autonomous Systems can be done with just Machine Learning (ML)? Should most requirements remain rule-based, and if so – how does that connect to the ML part? And how will the uneasy interface between ML and rules influence general ML-based systems?...
  • Privacy-preserving generative deep neural networks support clinical data sharing
    Though it is widely recognized that data sharing enables faster scientific progress, the sensible need to protect participant privacy hampers this practice in medicine. We train deep neural networks that generate synthetic subjects closely resembling study participants. Using the SPRINT trial as an example, we show that machine-learning models built from simulated participants generalize to the original dataset...
  • The Confluence of Geometry and Learning
    The learning signal for our 3D perception capability likely comes from making consistent connections among different perspectives of the world that only capture partial evidence of the 3D reality. We present methods for building 3D prediction systems that can learn in a similar manner...
  • Lessons learned from building a Hello World Neural Network
    I remember myself impressed by a model that generates natural language descriptions of images and their regions, developed at the Stanford University in 2015, thinking that I would like to be able to do similar things at some point. So I started searching...
  • Recommendation System Algorithms
    Today, many companies use big data to make super relevant recommendations and growth revenue. Among a variety of recommendation algorithms, data scientists need to choose the best one according a business’s limitations and requirements. To simplify this task, the Statsbot team has prepared an overview of the main existing recommendation system algorithms....
  • Controlling Linguistic Style Aspects in Neural Language Generation
    Most work on neural natural language generation (NNLG) focus on controlling the content of the generated text. We experiment with controlling several stylistic aspects of the generated text, in addition to its content. The method is based on conditioned RNN language model, where the desired content as well as the stylistic parameters serve as conditioning contexts...


  • Data Scientist - Hello Fresh - Berlin, Germany
    We are looking for a smart, result-oriented individual who can translate data insights into recommendations driving high-end business value across areas of demand management, marketing, customer lifecycle, and product development. Our ideal candidate has solid background in data science, including predictive modelling, forecasting and validation techniques. So if you are passionate about finding answers in scientific investigation and leading new solutions, feel invited to apply!...

Training & Resources

  • Neural Networks
    Nice collection of slides & pointers on poorly understood / unintuitive properties of Neural Networks...


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