Data Science Weekly Newsletter - Issue 159

Issue #159

Dec 8 2016

Editor Picks
 
  • The major advancements in Deep Learning in 2016
    Deep Learning has been the core topic in the Machine Learning community the last couple of years and 2016 was not the exception. In this article, we will go through the advancements we think have contributed the most (or have the potential) to move the field forward and how organizations and the community are making sure that these powerful technologies are going to be used in a way that is beneficial for all...
  • Six maps that show the anatomy of America’s vast infrastructure
    Trump’s plan to invest about $550 billion in new infrastructure projects across the country was a central theme in his campaign. The maps you are about to see show the massive scope of America’s infrastructure using data from OpenStreetMap and various government sources. They provide a glimpse into where that half-trillion dollars may be invested...
 
 

A Message from this week's Sponsor:

 

 
 

Data Science Articles & Videos

 
  • Artificial Intelligence Invades the Home … In Toys
    The first thing I learned about Cozmo is that it doesn’t like to stay put very long. Roused from slumber, the little robot’s face illuminates, and it begins zooming around the table in front of me. A moment later, it notices I’m watching and turns to greet me, saying my name with a computerized chirp...
  • Generative Art and Hamiltonian Monte Carlo
    [Talking Machines Episode] we talk about Hamiltonian Monte Carlo, we take a listener question about unbalanced data, plus we talk with Doug Eck of Google’s Magenta project...
  • This AI Boom Will Also Bust
    The bottom line here is that while some see this new prediction tech as like a new pipe tech that could improve all pipes, no matter their size, it is actually more like a tech only useful on very large pipes. Just as it would be a waste to force a pipe tech only useful for big pipes onto all pipes, it can be a waste to push advanced prediction tech onto typical prediction tasks. And the fact that this new tech is mainly only useful on rare big problems suggests that its total impact will be limited...
  • Predicting with confidence: Best machine learning idea you never heard of
    One of the disadvantages of machine learning as a discipline is the lack of reasonable confidence intervals on a given prediction. There are all kinds of reasons you might want such a thing, but I think machine learning and data science practitioners are so drunk with newfound powers, they forget where such a thing might be useful...
 
 

Jobs

 
  • Data Science Initiative, Scientific Lead - UCSF Library - San Franscisco, CA

    The UCSF Library’s Data Science Initiative is hiring! We are looking for a biomedical researcher with an entrepreneurial spirit and a passion for programming in R/Python, bioinformatics, data curation, statistics, data visualization - or all of the above – to serve as the Scientific Lead for our Data Science Initiative. We are taking a broad approach to data science, and are looking for someone who will work to identify the data science needs of the UCSF research community, help build a Library-based hub for data science activities, develop programs and events, and teach workshops and classes...
 
 

Training & Resources

 
  • Tidy Data in Python
    In this post, I will summarize some tidying examples Wickham uses in his paper and I will demonstrate how to do so using the Python pandas library...
 
 

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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