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Data Science Weekly Newsletter
August 3, 2017

Editor's Picks

  • Inside Salesforce’s Quest to Bring Artificial Intelligence to Everyone
    Starting two years ago, a band of artificial-intelligence acolytes within Salesforce escaped the towering headquarters with the goal of crazily multiplying the impact of the machine learning models that increasingly shape our digital world—by automating the creation of those models. As shoppers checked out sofas above their heads, they built a system to do just that...
  • Machines Are Developing Language Skills Inside Virtual Worlds
    Now teams at DeepMind, an AI-focused subsidiary of Alphabet, and Carnegie Mellon University have developed a way for machines to figure out simple principles of language for themselves inside 3-D environments based on first-person shooter computer games...

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Data Science Articles & Videos

  • Machine Learning Infrastructure at Stripe
    Machine learning at Stripe has a foundation built on Python and the PyData stack, with scikit-learn and pandas continuing to be core components of an ML pipeline that feeds a production system written in Scala. This talk will cover the ML Infra team’s work to bridge the serialization and scoring gap between Python and the JVM, as well as how ML Engineers ship models to production...
  • Fashioning with Networks: Neural Style Transfer to Design Clothes
    In this paper, the neural style transfer algorithm is applied to fashion so as to synthesize new custom clothes. We construct an approach to personalize and generate new custom clothes based on a users preference and by learning the users fashion choices from a limited set of clothes from their closet...
  • The AI Hierarchy of Needs
    Think of AI as the top of a pyramid of needs. Yes, self-actualization (AI) is great, but you first need food, water and shelter (data literacy, collection and infrastructure)...
  • Natural Language Processing with Small Feed-Forward Networks
    We show that small and shallow feed-forward neural networks can achieve near state-of-the-art results on a range of unstructured and structured language processing tasks while being considerably cheaper in memory and computational requirements than deep recurrent models...
  • Earth from Space
    Analyzing DigitalGlobe’s high resolution satellite data to obtain a detailed representation of land use on Earth....
  • Predicting Personality from Book Preferences with User-Generated Content Labels
    Psychological studies have shown that personality traits are associated with book preferences. However, past findings are based on questionnaires focusing on conventional book genres and are unrepresentative of niche content. For a more comprehensive measure of book content, this study harnesses a massive archive of content labels, also known as 'tags', created by users of an online book catalogue,


  • Junior Data Scientist/Data Scientist - Penguin Random House US - New York City, USA
    The Data Science & Analytics group at Penguin Random House is seeking a Junior Data Scientist/Data Scientist...In this role, you will have an opportunity to work on a variety of high-profile projects under the mentorship of Senior Data Scientists and in collaboration with key decision makers across the organization...We are an agile team of data scientists and software engineers. The team has a wide mandate encompassing pricing systems, recommendation / personalization systems, title segmentation, supply chain, as well as ad-hoc analysis and data exploration....

Training & Resources

  • Deep recommender models using PyTorch.
    Spotlight uses PyTorch to build both deep and shallow recommender models. By providing both a slew of building blocks for loss functions (various pointwise and pairwise ranking losses), representations (shallow factorization representations, deep sequence models), and utilities for fetching (or generating) recommendation datasets, it aims to be a tool for rapid exploration and prototyping of new recommender models...


  • Text Mining with R: A Tidy Approach
    Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools. With this practical book, you’ll explore text-mining techniques with tidytext, a package that authors Julia Silge and David Robinson developed using the tidy principles behind R packages like ggraph and dplyr. You’ll learn how tidytext and other tidy tools in R can make text analysis easier and more effective....

    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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