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Data Science Weekly Newsletter
October 26, 2017

Editor's Picks

  • AlphaGo, in context
    I had a chance to talk to several people about the recent AlphaGo matches with Ke Jie and others. In particular, most of the coverage was a mix of popular science + PR so the most common questions I’ve seen were along the lines of “to what extent is AlphaGo a breakthrough?”, “How do researchers in AI see its victories?” and “what implications do the wins have?”. I thought I might as well serialize some of my thoughts into a post...
  • How to unit test machine learning code.
    Over the past year, I’ve spent most of my working time doing deep learning research and internships. And a lot of that year was making very big mistakes that helped me learn a lot about not just about ML, but about how to engineer these systems correctly and soundly. One of the main principles I learned during my time at Google Brain was that unit tests can make or break your algorithm and can save you weeks of debugging and training time...

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Data Science Articles & Videos

  • How Adversarial Attacks Work
    Recent studies by Google Brain have shown that any machine learning classifier can be tricked to give incorrect predictions, and with a little bit of skill, you can get them to give pretty much any result you want. This fact steadily becomes worrisome as more and more systems are powered by artificial intelligence — and many of them are crucial for our safe and comfortable life. Lately, safety concerns about AI were revolving around ethics — today we are going to talk about more pressuring and real issues...
  • Stop Using word2vec
    Word vectors are awesome but you don’t need a neural network – and definitely don’t need deep learning – to find them2. So if you’re using word vectors and aren’t gunning for state of the art or a paper publication then stop using word2vec...
  • First assessment of learning-to-rank
    The Search Platform Team has been working on improving search on Wikimedia projects with machine learning. Machine learned-ranking (MLR) enables us to rank relevance of pages using a model trained on implicit and explicit judgements. In the first test of the learning-to-rank (LTR) project, we evaluated the performance of a click-based model on users searching English Wikipedia...
  • How Columns in Neocortex Enable Learning the Structure of the World
    It is widely observed that movement affects how we sense objects in the world, but how this happens in the brain has remained a mystery. In this paper, we propose a network model that learns the structure of objects through movement. Our model is based on the known biology of cortical columns and layers, and helps explain their function...
  • Dissolving the Fermi Paradox
    The Fermi question is not a paradox: it just looks like one if one is overconfident in how well we know the Drake equation parameters...


  • Data Scientist - MealPal - New York
    Are you passionate about helping an organization make smart decisions in order to deliver the best product and user experience? Do you want to join a fast-paced, growing company? As a Data Scientist at MealPal, you will focus on using data to drive business strategy and take our company to the next level. You will have the opportunity to think critically and problem solve in order to drive valuable and executable insights...

Training & Resources

  • Lasso with d3 v4 and Canvas
    Example of a lasso technique using data drawn on canvas (or really anywhere) and an SVG interaction layer for drawing the lasso...


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