Data Science Weekly Newsletter - Issue 99

Issue #99

October 15 2015

Editor Picks
  • Auto-Generating Clickbait With Recurrent Neural Networks
    “F.D.R.’s War Plans!” reads a headline from a 1941 Chicago Daily Tribune. Had this article been written today, it might rather have said “21 War Plans F.D.R. Does Not Want You To Know About. Number 6 may shock you!”. Modern writers have become very good at squeezing out the maximum clickability out of every headline. But this sort of writing seems formulaic and unoriginal. What if we could automate the writing of these, thus freeing up clickbait writers to do useful work?...

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Data Science Articles & Videos

  • Applications of Machine Learning in FinTech
    There are various applications of machine learning used by the FinTech companies falling under different subcategories. Let us look at some of the applications of machine learning and companies using such applications...
  • Which NFL Teams Are the Biggest Surprises of 2015 So Far?
    We’re now 4.0625 weeks into the NFL’s 2015 regular season. (If you don’t know what the NFL is, you should probably stop reading now.) That’s about one-quarter of the whole 256-game shebang, enough to start taking stock of preseason predictions. So I got to wondering: Which teams have been the biggest surprises so far?...
  • Catcierge
    Image recognition (to keep cat prey out) and RFID chip reader system for automated DIY cat door...
  • Machine learning for model selection in population genomics
    In a recent preprint posted on bioRxiv, Sara Sheehan and Yun Song present a likelihood-free inference framework for population genomics that applies deep learning, an active area of machine learning research. They aimed to jointly infer natural selection and changes in population size, processes that can leave similar signatures in the genome, by testing their method on simulated data and on empirical data for Drosophila melanogaster...
  • Differentiable Memory
    Reference implementations of various differentiable memory schemes for neural networks, in pure numpy. These are meant to serve as correctness checks against which higher performance batched GPU implementations can be evaluated...
  • OpenFace: Face recognition with Google's FaceNet deep neural network
    This is a Python and Torch implementation of the CVPR 2015 paper FaceNet: A Unified Embedding for Face Recognition and Clustering by Florian Schroff, Dmitry Kalenichenko, and James Philbin at Google using publicly available libraries and datasets. Torch allows the network to be executed on a CPU or with CUDA...
  • New Report Puts Numbers on Data Scientist Trend
    Data scientist – a job that barely existed a decade ago – has become one of the hottest and best-paid professions in the U.S. Companies say they need people who have the skill sets – both business and technical – to analyze the rising tide of data produced by customers and operations. RJMetrics, a software startup that itself is looking for data scientists to fill open positions, dove into LinkedIn data to gauge the field’s scope...


  • Data Scientist: Experimentation - RelayRides - San Francisco, CA

    We are looking for a big data artist to join our exceptional Analytics and Data Science team. If you’re interested in experimentation design and hypothesis testing to power a rapidly growing business, we have the perfect role for you. Be at the intersection of Big Data, Modelisation and Business Intelligence at RelayRides, and help our modeling, product, marketing, and operations teams figure out answers to their most challenging questions. The more data insights we have, the happier we are...

Training & Resources

  • Machine Learning for Developers
    Most developers these days have heard of machine learning, but when trying to find an 'easy' way into this technique, most people find themselves getting scared off by the abstractness of the concept...
  • Visual Information Theory
    Unfortunately, information theory can seem kind of intimidating. I don’t think there’s any reason it should be. In fact, many core ideas can be explained completely visually!...



  • Data Scientists at Work

    Collection of interviews with sixteen of the world's most influential and innovative data scientists...

    "Excellent book. It was fascinating to learn how the great minds behind of our most popular Internet sites evolved and are affecting our future..."

    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
P.S. Interested in reaching fellow readers of this newsletter? Consider sponsoring! Email us for details :) - All the best, Hannah & Sebastian
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