Data Science Weekly Newsletter - Issue 106

Issue #106

December 3 2015

Editor Picks
 
  • Deep Forger: Art Forgery Meets Deep Neural Nets
    The past year has seen deep learning make exceptional advances in imaging, perhaps most notably with Google's Deep Dream. See how a clever Twitter bot employs deep neural nets to paint images in the style of famous painters...
 
 

A Message from this week's Sponsor:

 

  • [WEBINAR] Create Richly Interactive Visualizations with Open Source

    Learn how to make richly interactive data visualizations for your open data science project with Anaconda and Bokeh. The webinar will be presented by Peter Wang, CTO & Co-founder of Continuum Analytics. Peter is the creator of Bokeh, the interactive visualization framework.

    Join Us for the Webcast on December 15th 

 

Data Science Articles & Videos

 
  • Data-mined photos document 100 years of (forced) smiling
    By studying nearly 38,000 high-school yearbook photos taken since 1905, UC Berkeley researchers have shown just how much smiling, fashion and hairstyles have changed over the years. The goal was not just to track trends, but figure out how to apply modern data-mining techniques and machine learning to a much older medium: photographs...
  • Wikipedia Bets On AI To Rebuild Editor Ranks
    Wikipedia will leverage a new machine learning service called ORES to automate reviews of revisions to flag ones that are problematic and make it easier for human editors to get their proposed revisions approved...
  • Simple end-to-end TensorFlow examples
    I flew from Austin to Washington DC last week, and the morning before my flight I downloaded TensorFlow, made sure everything compiled, downloaded the necessary datasets, and opened up a bunch of tabs with TensorFlow tutorials. My goal was, while on the airplane, to run the tutorials, get a feel for the flow of TensorFlow, and then implement my own networks for doing some made-up classification problems. I came away from the exercise extremely pleased. This post explains what I did and gives pointers to the code to make it happen...
  • Is Bayesian A/B Testing Immune to Peeking? Not Exactly
    Since I joined Stack Exchange as a Data Scientist in June, one of my first projects has been reconsidering the A/B testing system used to evaluate new features and changes to the site. Our current approach relies on computing a p-value to measure our confidence in a new feature. Unfortunately, this leads to a common pitfall...
  • Regularizing RNNs by Stabilizing Activations
    We stabilize the activations of Recurrent Neural Networks (RNNs) by penalizing the squared distance between successive hidden states' norms. This penalty term is an effective regularizer for RNNs including LSTMs and IRNNs, improving performance on character-level language modelling and phoneme recognition, and outperforming weight noise...
  • Machine Intelligence In The Real World
    I’ve been laser-focused on machine intelligence in the past few years. I’ve talked to hundreds of entrepreneurs, researchers and investors about helping machines make us smarter. On average, people seem most concerned about how to interact with these technologies once they are out in the wild. This post will focus on how these companies go to market, not on the methods they use....
 
 

Jobs

 
  • Data Scientist, Smart Pricing - Walmart eCommerce - Sunnyvale, CA

    We are a highly motivated group of Big Data engineers, Data Scientists and Applications Engineers, working in small agile groups to solve sophisticated and high impact problems. We are building systems that ingest, model and analyze massive flow of data from online, social, mobile and offline commerce/user activity to set key business attributes for millions of products in real time. We use cutting edge machine learning, data mining and optimization algorithms underneath it all to analyze all this data on top of Hadoop/HBase/Hive. Your work will be immediately visible to millions of people and you will have a direct impact on the business goals of Fortune #1 company. If you talk, speak and think data we want to talk to you. Come join our small team and be part of this exciting journey...
 
 

Training & Resources

 
  • awesome-nlp
    A curated list of resources dedicated to Natural Language Processing...
  • U.S. Government’s open data
    Here you will find data, tools, and resources to conduct research, develop web and mobile applications, design data visualizations, and more - over 188K datasets...
 
 

Books

 

  • Python Data Science Cookbook

    New release and getting very good reviews...

    "This book gives a very practical approach to learn some of the important algorithms using Python. Great hands on experience. I am sure once you do the examples in this book, most of the fundamental concepts will be understood..."

    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
 
 
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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