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Data Science Weekly Newsletter
Issue
245
August 2, 2018

Editor's Picks

  • Hiring for Autonomy in Data Science Teams
    In our latest episode of the In Context Podcast, we're joined by Eric Colson, the Chief Algorithms Officer for Stitch Fix, an online subcription personal shopping service whose tech blog, Multithreaded, may have the coolest algorithms tour on the internet. In a fascinating discussion, he and Kathryn Hume analyze how Stitch Fix's engineering culture works, including what they value, what they look for in new hires, and how they’ve architected their platform to enable astounding success. You'll also hear about the critical role that autonomy plays in how Eric organizes his data science teams...



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Data Science Articles & Videos

  • Ten Techniques Learned From fast.ai
    Right now, Jeremy Howard – the co-founder of fast.ai – currently holds the 105th highest score for the plant seedling classification contest on Kaggle, but he's dropping fast. Why? His own students are beating him. And their names can now be found across the tops of leaderboards all over Kaggle....
  • Using Uncertainty to Interpret your Model
    Why uncertainty estimates are important in DL; Which types of uncertainty exist (data, model, measurement, label); Examples of how we can use uncertainty to interpret our model, and how we can use it for debugging...



Jobs

  • Data Scientist (Product) - Spotify - NYC
    We are looking for a Data Scientist to join the band and help drive a data-first culture across Spotify. As a Data Scientist, our mission is to turn terabytes of data into insights and get a deep understanding of how our people use our apps to impact the product, strategy and direction of Spotify. You will study user behavior, strategic initiatives, content, and new features and bring data and insights into every decision we make. Above all, your work will impact the way the world experiences music...


Training & Resources

  • The Hitchiker's Guide to PyTorch
    The rest of this document, based on the official MNIST example, is about grokking PyTorch, and should only be looked at after the official beginner tutorials. For readability, the code is presented in chunks interspersed with comments, and hence not separated into different functions/files as it would normally be for clean, modular code...


Books



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