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Data Science Weekly Newsletter
December 15, 2016

Editor's Picks

  • What Neural Network Can Tell About Your Doodles?
    At this point in time, neural nets like Google’s “Quick, Draw,” are still learning to recognize people’s drawings. And that’s just the beginning. Soon they will be able to analyze them. Just like I did, but using bigger data...
  • Where Should Machines Go To Learn?
    Past civilizations built grand libraries to organize the world’s knowledge. These repositories of information focused on cataloging, aggregating, organizing and making information accessible so that others could focus on learning and creating new knowledge. AI and machine learning systems also need repositories of information from which to learn — and right now everyone is building their own...

A Message From This Week's Sponsor

  • We built Rodeo
    to make Python easier to use. It's open source & free. Support for Windows, Mac & Linux. Happy holidays! -The Yhat team.

Data Science Articles & Videos

  • The Great A.I. Awakening
    How Google used artificial intelligence to transform Google Translate, one of its more popular services — and how machine learning is poised to reinvent computing itself...
  • Tourists Vs Locals: 20 Cities Based On Where People Take Photos
    Tourists and locals experience cities in strikingly different ways. For example, in London most tourists will visit Buckingham Palace, the Houses of Parliament, St. Paul’s Cathedral, Oxford Street, etc. but will probably not end up visiting East Croydon, Hendon, Dagenham or any of the other parts of London outside of Zone 1...
  • Wall Street wants algorithms that trade based on Trump's tweets
    President elect Donald Trump's ability to move the stock market with a tweet became readily apparent yesterday after an early morning tweet about Lockheed Martin's F-35 fighter jet program caused the company to lose $4 billion in market value. While that's a major loss for the company, it actually presents an opportunity for traders on Wall Street, some of whom have already started looking for ways to easily cash in on the volatility Trump brings to the market...
  • Type Safety and Statistical Computing
    I broadly believe that the statistics community would benefit from greater exposure to computer science concepts. Consistent with that belief, I argue in this post that the concept of type-safety could be used to develop a normative theory for how statistical computing systems ought to behave...
  • Data-derived Products
    In this installment, I discuss some in-the-weeds approaches to creating data-derived products. For me, this means using metadata/logs, productized for a non-adjacent market and selling something of value. This requires technical, business and creative skills...


  • Software Engineer / Sr Data Scientist - Slice Technologies - San Mateo, CA
    Slice Intelligence is looking for a skillful engineer (or senior data scientist) to add to its Data Science team. Either a developer with some data science exposure and interest, or a senior data scientist with top coding and engineering skills and an interest to focus on development would be a fit. This Engineer is not someone to whom mundane tasks are tossed as a Doer in a Thinker / Doer dichotomy: Slice Intelligences Data Scientists and Engineers are both Thinker-Doers! So you wont be re-writing hopeless broken Python code into Java and/or scheduling ETL jobs...

Training & Resources

  • PyData, The Complete Works
    The unofficial index of all PyData talks. This was intially going to be a pickled pandas DataFrame object, but then I decided against it. So here it is - in beautiful Github flavored markdown...
  • How to Train a GAN? Tips and tricks to make GANs work
    While research in Generative Adversarial Networks (GANs) continues to improve the fundamental stability of these models, we use a bunch of tricks to train them and make them stable day to day. Here are a summary of some of the tricks...


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