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Data Science Weekly Newsletter
May 12, 2016

Editor's Picks

  • Machine Learning for Emoji Trends
    In Part 1 of this blog post series, we will take a deep dive into emoji usage on Instagram. By applying machine learning and natural language processing techniques, we’ll discover the hidden semantics of emoji...
  • The next AI is no AI
    AI technologies could evolve into a platform, an infrastructure similar to the Internet, that would allow people themselves to decide the way they utilize AI or contribute to its design and development. Such an AI grid, like the Internet of Things², powering various experiences and applications in different environments and industries, being open for tinkerers and specialists alike, would significantly change the way we could understand AI or interact with intelligent systems in general...

A Message From This Week's Sponsor

  • Catenus Science Apprenticeship Program

    The Catenus Science Apprenticeship Program identifies top data scientists who will raise the bar when hired at a startup. To help meet this goal, the program will train qualified candidates to have immediate, meaningful impact as data scientists in some of the top data startups in the world. This program will hone their skills in statistics, machine-learning, programming, and product development by presenting them with real-world challenges put forth by startups in Silicon Valley and the Bay Area.
    We offer a fully-paid, 13-week apprenticeship
    during which we reinforce technical and business skills. We do this via a mix of formal instruction and hands-on application of data science in some of the best startups in the world...

Data Science Articles & Videos

  • March Machine Learning Mania 2016, Winner's Interview:
    1st Place, Miguel Alomar

    The annual March Machine Learning Mania competition sponsored by SAP challenged Kagglers to predict the outcomes of every possible match-up in the 2016 men's NCAA basketball tournament. Nearly 600 teams competed, but only the first place forecasts were robust enough against upsets to top this year's bracket. In this blog post, Miguel Alomar describes how calculating the offensive and defensive efficiency played into his winning strategy...
  • Number plate recognition with Tensorflow
    In order to get some hands-on experience with implementing neural networks I decided I’d design a system to solve a similar problem: Automated number plate recognition (automated license plate recognition if you’re in the US)...
  • Towards Conceptual Compression
    We introduce a simple recurrent variational auto-encoder architecture that significantly improves image modeling. The system represents the state-of-the-art in latent variable models for both the ImageNet and Omniglot datasets...


  • Data Scientist - DataKind - New York
    We’re looking for a self-directed data scientist to work with our full-time DataKind Labs team in delivering insights and quantitative expertise to our sector-wide initiatives. As a member of DataKind Labs, you will be working directly with our Head of Labs in this highly visible and high impact role, which will be equal parts challenging and rewarding. DataKind Labs projects are multi-party, year-long, full-time initiatives designed to bring innovative machine learning and data science solutions to social challenges...

Training & Resources

  • What is Machine Learning — Explain like I am 5 years old
    So as co-founder of a startup whose core offerings are AI technologies, I find myself explaining Machine Learning (or worse Deep Learning techniques that we use) to a lot of people (clients, potential investors, other enthusiasts) what exactly is Machine Learning and how can we use it. Here I will try to present the same points...


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