Data Science Weekly Newsletter - Issue 103

Issue #103

November 12 2015

Editor Picks
 
  • Here's How Smart Facebbok's AI Has Become
    Watch Yann LeCun, director of AI at Facebook, show off some of what the AI that powers Facebook M is capable of yesterday at MIT Technology Review’s EmTech conference...
  • Five Hundred Deep Learning Papers, Graphviz and Python
    I invested days creating a graph with PyGraphviz, representing the evolutionary process of deep learning’s state of the art for the last twenty-five years, or at least this was my objective. This is the final result...
  • Looking back at 9 years of Hacker News
    Besides serving as the holy grail of daily updates of what's going on in the tech world, HN has, over time, managed to accumulate a history of what tech talks about, what tech cares about, and the progress tech has made in the recent past. In this post, I look at interesting things the data from HN can tell us...
 
 

A Message from this week's Sponsor:

 

  • Tune Up Your Data Science Process (Free Webinar, December 9th)
    Businesses increasingly use data science to foster data-driven decision making. Unfortunately, many data scientists are doing "bad science," which leads to expensive mistakes. In this free webinar, you'll learn a framework to ensure correctness in scientific modeling and computation, and simple techniques to produce higher-quality work. Learn more and reserve your spot today!

     

 

Data Science Articles & Videos

 
  • Regressing Images: Regressing 24 Hours in New Orleans
    Regression is a widely applied technique in machine learning. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. Regression analysis is a statistical process for estimating the relationships among variables. Lets have some fun with it ;-)...
  • Ramanujan surprises again
    A box of manuscripts and three notebooks. That's all that's left of the work of Srinivasa Ramanujan, an Indian mathematician who lived his remarkable but short life around the beginning of the twentieth century. Yet, that small stash of mathematical legacy still yields surprises...
  • Strength in Numbers: Why Golden State Deserved to Win it All
    The Golden State Warriors won the NBA finals last year and posted the best record in the regular season. Yet some people have argued that their success was based on luck and that playing “small ball” is a recipe that only works in the regular season. Here at the Stitch Fix algorithms team we have several devoted Warriors fans and hence we had to investigate these claims – from a data science point of view, of course...
  • Expected Goals Just Don’t Add Up — They Also Multiply
    Soccer and hockey analytical communities have been pleased to discover some measure of shot quality in their sports. However, these values are only adding expected goals. But something is missing. Only adding independent probabilities misses half of the story: variance...
  • A Data Genius has figured out the Ultimate Beer-Drinking Road Trip
    So much beer, so little time. If you're a beer lover who lives in the U.S., you might be familiar with this problem. The U.S. has tons of fantastic breweries -- 3,464 in total in 2014, about 1,800 of which were microbreweries, according to the Brewer's Association. So Nathan Yau, who runs the blog Flowing Data, has performed a great public service -- finding the top-rated breweries in the continental U.S., and then creating a map for a road trip route that will take you to all of them in the fewest miles possible...
  • Happy? Sad? Angry? This Microsoft tool recognizes emotions in pictures
    Humans have traditionally been very good at recognizing emotions on people’s faces, but computers? Not so much. That is, until now. Recent advances in the fields of machine learning and artificial intelligence are allowing computer scientists to create smarter apps that can identify things like sounds, words, images – and even facial expressions...
  • How To Find Junior Data Scientist Jobs
    You've started looking for a Junior Data Science job on job websites. Almost all of the jobs you've found posted are from organizations who are looking for senior level data scientists. While you're confident that you shouldn't have any problems learning some new things and are currently learning everything you can about data science, you are looking for a junior position not a senior position. The problem you're running into is that it's hard to find junior data scientist jobs...
 
 

Jobs

   
 

Training & Resources

 
  • Building Interactive Dashboards with Jupyter
    Welcome to Part II of "Advanced Jupyter Notebook Tricks." In Part I, I described magics, and how to calculate notebooks in "batch" mode to use them as reports or dashboards. In this post, I describe another powerful feature of Notebooks: the ability to use interactive widgets to build interactive dashboards...
  • 6 TED Talks on AI
    Computers are being taught to learn, reason and recognize emotions. In these talks, look for insights -- as well as warnings...
  • Smoothing data with Julia’s @generated functions
    One of Julia’s great strengths for technical computing is its metaprogramming features, which allow users to write collections of related code with minimal repetition. One such feature is generated functions, a feature recently implemented in Julia 0.4 that allows users to write customized compute kernels at “compile time”...
 
 

Books

 

 
 
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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