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Data Science Weekly Newsletter
July 26, 2018

Editor's Picks

  • One Data Science Job Doesn’t Fit All
    At Airbnb, wee recently established a role-defining framework. My hope is that what we've learned along the way can help other companies be strategic in defining data science roles. The main takeaway I will share is that companies consider three tracks of data science work to meet the needs of your business — Analytics, Inference, and Algorithms. Below I'll describe the evolution of how we came to these three tracks of work and how it helps...
  • Google's AutoML: Cutting Through the Hype
    In today’s post, I want to look specifically at Google’s AutoML, a product which has received a lot of media attention, and address the following: What is Google's AutoML? What is transfer learning? Why all the hype about Google's AutoML?...
  • uTensor - Test Release: AI inference library based on mbed and TensorFlow
    uTensor is an extremely light-weight machine learning inference framework built on Mbed and Tensorflow. The project contains a runtime library and an offline tool. The total size of graph definition and algorithm implementation of a 3-layer MLP produced by uTensor is less than 32kB in the resulting binary (excluding the weights)...

A Message From This Week's Sponsor

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Data Science Articles & Videos

  • New Research on Multi-Task Learning
    Multi-task learning is an alternative approach to training machine learning algorithms that allows machines to master more than one task; machines gain the ability to benefit from task relationships. Machine learning becomes, a little bit more, like human learning - capable of taking on more complex challenges involving richer representations of reality...
  • signboardr
    Utilize Google Vision API to extract text from archaeological photos containing a sign board. Further, the extracted text can be added as searchable XMP metadata tags to photos...
  • A New Hope: AI for News Media
    If the news media wants to affect how news content is created, developed, presented and delivered to us in the future, they need to take an active role in AI development. If news organizations want to understand the way data and information are constantly affected and manipulated in digital environments, they need to start embracing the possibilities of machine learning...
  • Building your own Duplex AI agent using Rasa and Twilio
    In this article I’m going to illustrate how you can build your own Duplex-like agent to handle phone calls autonomously. We’re going to approach the problem at hand from the other direction — calling a business and talking with a machine (rather than a machine calling a business)...


  • 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

  • Using Python to Figure out Sample Sizes for your Study
    Understanding the sample size you need depends on the statistical test you plan to use. If it’s a straightforward test, then finding the desired sample size can be just a matter of plugging numbers into an equation. However, it can be more involved, in which case a programming language like Python can make life easier. In this post, I’ll go through one of these more difficult cases...


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