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Data Science Weekly Newsletter
March 1, 2018

Editor's Picks

  • The Hit Charade: Challenge of Algorithmic Creativity
    Just as computers cannot yet create powerful and imaginative art or prose, they cannot truly appreciate music. And arranging a poignant or compelling music playlist takes a type of insight they don’t have—the ability to find similarities in musical elements and to get the emotional resonance and cultural context of songs. For all the progress being made in artificial intelligence, machines are still hopelessly unimaginative and predictable. This is why Apple has hired hundreds of people to serve as DJs and playlist makers, in addition to the algorithmic recommendations it still offers...
  • Pass the Butter // Pancake bot
    My goal is to train a robotic arm to make pancakes. As a first test, curriculum learning was used to get the arm to toss a pancake onto a plate. My motivation for the project is my complete lack of cooking ability...

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Data Science Articles & Videos

  • Piccolo is building a gesture-based smart home ‘vision assistant’
    Voice assistants may be the hottest thing since sliced bread when it comes to controlling your $60 Wi-Fi light bulbs, but Piccolo is launching out of the latest Y Combinator class with a desire to put a camera in every smart home that can translate your physical motions and gestures into commands...
  • A Comparative Analysis of Top 6 BI and Data Visualization Tools in 2018
    Nowadays, there is a huge list of powerful visualization tools to help you illustrate your ideas, visualize your data, make it talk, share your significant analytics with customers and the global community. In this article, we will compare the most commonly used platforms and analyze their main features to help you choose one or several platforms that will provide indispensable aid for your work communication...
  • Neural Spelling Corrections and the Importance of Accuracy
    The days of ‘query engineering’ are almost a thing of the past, made obsolete by Google’s ability to almost intuitively know what we are actually hoping to find. For better or worse, Google has trained us to expect great results with a simple set of keywords, malformed questions, and careless spelling. The last point is something I would like to discuss in a bit more detail, and how we at Scribd attempted to solve it using neural networks...
  • DeepPavlov
    Open-source library for building chatbots, built on top of TensorFlow & Keras...


Training & Resources

  • Calculate Column Sum In TensorFlow
    Learn how to do a column sum in TensorFlow using tf.reduce_sum to get the sum of all of the elements in the columns of a Tensor, via a screencast video and full tutorial transcript...
  • Learn with Google AI
    Whether you're just learning to code or you're a seasoned machine learning practitioner, you'll find information and exercises in this resource center to help you develop your skills and advance your projects...
  • How to build a deep learning model in 15 minutes
    As Instacart has grown, we’ve learned a few things the hard way. We’re open sourcing Lore, a framework to make machine learning approachable for Engineers and maintainable for Machine Learning Researchers...


  • Bit by Bit: Social Research in the Digital Age
    "The book goes well beyond "big data" to unpack the possibilities of doing social science research at a massive scale, and relatively inexpensively. This book should be read by social scientists who want to expand their research horizons, data scientists who want to understand how to incorporate the insights of social science, and anyone in a line of work in which they have potential data that can give them insights into how people behave..."...
    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page...

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