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Data Science Weekly Newsletter
December 1, 2016

Editor's Picks

  • The Simple Economics of Machine Intelligence
    As economists, we believe some simple rules apply. Technological revolutions tend to involve some important activity becoming cheap, like the cost of communication or finding information. Machine intelligence is, in its essence, a prediction technology, so the economic shift will center around a drop in the cost of prediction...

A Message From This Week's Sponsor

  • Level Up Your Python Workflow & Get Notebooks You Can Share

    Mode is a SQL editor, Python notebook, and visualization builder all rolled into one. Explore data with SQL and pass results instantly into a Python notebook for further exploration and visualization. Pick and choose output cells to present to others, or send the whole notebook—you can even share with people who don't have a Python environment set up.

Data Science Articles & Videos

  • AI Machine Attempts to Understand Comic Books ... and Fails
    The list of activities in which artificial intelligence machines have bested humans is increasing at an alarming rate. Face recognition, object recognition, chess, Go, various video games, and numerous other tasks have all fallen in this battle. So it’s natural to ask about the types of tasks that machines still have difficulty with. Where do humans still rule the roost?...
  • How the Trump Campaign Built an Identity Database and Used Facebook Ads to Win the Election
    There may be some fake news on Facebook, but the power of the Facebook advertising platform to influence voters is very real. This is the story of how the Trump campaign used data to target African Americans and young women with $150 million dollars of Facebook and Instagram advertisements in the final weeks of the election, quietly launching the most successful digital voter suppression operation in American history...
  • Fast Face-swap Using Convolutional Neural Networks
    We consider the problem of face swapping in images, where an input identity is transformed into a target identity while preserving pose, facial expression, and lighting. To perform this mapping, we use convolutional neural networks trained to capture the appearance of the target identity from an unstructured collection of his/her photographs...
  • Improving variational approximations
    Nick Foti, Ryan Adams, and I just put a paper on the arxiv about improving variational approximations (short version accepted early to AABI2016). We focused on one problematic aspect of variational inference in practice — that once the optimization problem is solved, the approximation is set and there isn’t a straightforward way to improve it, even when we can afford some extra compute time...
  • Plotting Earthquakes with D3.js + Leaflet
    I am still learning d3.js, and thought it would be a good idea to share with you my trial and error process (admittedly, sometimes more error than trial) when doing the earthquake visualization. Here is the visualization. I later go through some of the steps I took to complete it...
  • Decoding The Thought Vector
    In this blog post I put forward a possible interpretation of these vectors. I argue we shouldn't take these vectors literally, but rather as an encoding for a simpler, sparse data structure. This gives rise to a simple technique (the -SVD) for reverse engineering this data structure, and gives us the tools to decode the vectors' meaning...


  • Senior Data Science Analyst - VSCO - Oakland, CA
    VSCO is a leading creative platform with a monthly audience of over 45 million and growing.
    We are looking for a Senior Data Science Analyst to build data at VSCO from the ground up. You will design our data model for user behavior, content impression, and mine the data to bring out insights that will influence the product roadmap. Expect to get your hands dirty with Redshift, Spark, and data visualization tools under the guidance of our Director of Data Science...

Training & Resources

  • Calculating AUC: the area under a ROC Curve
    In this post I’ll work through the geometry exercise of computing the area, and develop a concise vectorized function that uses this approach. Then we’ll look at another way of viewing AUC which leads to a probabilistic interpretation...
  • An Interactive Tutorial on Numerical Optimization
    I ended up writing a bunch of numerical optimization routines back when I was first trying to learn javascript. Since I had all this code lying around anyway, I thought that it might be fun to provide some interactive visualizations of how these algorithms work. The cool thing about this post is that the code is all running in the browser, meaning you can interactively set hyper-parameters for each algorithm, change the initial location, and change what function is being called to get a better sense of how these algorithms work....


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