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Data Science Weekly Newsletter
October 11, 2018

Editor's Picks

  • The ML Engineering Loop
    In this article, we’ll describe our conception of the “OODA Loop” of ML: the ML Engineering Loop, where ML Engineers iteratively: Analyze; Select an approach; Implement; Measure to rapidly and efficiently discover the best models and adapt to the unknown. In addition, we will give concrete tips for each of these phases, as well as to optimize the process as a whole...

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Data Science Articles & Videos

  • YouTube Trending Videos Analysis
    YouTube is the most popular and most used video platfrom in the world today. YouTube has a list of trending videos that is updated constantly. Here we will use Python with some packages like Pandas and Matplotlib to analyze a dataset that was collected over 205 days...
  • Reinforcement Learning for Improving Agent Design
    In many reinforcement learning tasks, the goal is to learn a policy to manipulate an agent, whose design is fixed, to maximize some notion of cumulative reward. The design of the agent’s physical structure is rarely optimized for the task at hand. In this work, we explore the possibility of learning a version of the agent’s design that is better suited for its task, jointly with the policy...
  • Deep Learning just tipped into Exascale Territory
    Today, researchers from Berkeley Lab and Oak Ridge, along with development partners at Nvidia demonstrated some rather remarkable results using deep learning to extract weather patterns based on existing high-res climate simulation data. This places the collaboration in the running for this year’s Gordon Bell Prize, an annual award based on high performance, efficient use of real-world applications that can scale on some of the world’s most powerful supercomputers...
  • Holodeck
    Holodeck is a high-fidelity simulator for reinforcement learning built on top of Unreal Engine 4, with an OpenAI-Gym interface...


  • Data Scientist - Pear Therapeutics - San Francisco or Boston

    At Pear Therapeutics, we have the privilege of building the world’s first-ever class of prescription digital therapeutics. By nature of our therapeutics as digital applications, we have access to rich datasets and unique opportunities to drive clinical outcomes. As a Data Scientist, you will be responsible for shaping and delivering data-driven insights. We are looking for data scientists with a deep product sense, who have an innate curiosity, and are eager to dive into large, complex datasets and create actionable insights....

Training & Resources

  • LensKit
    Today, we're presenting the next generation of LensKit: Python tools for recsys experiments...
  • Communication Primitives in Deep Learning Frameworks
    For large-scale training of neural networks you need to specify how the work is broken up across machines. This is where communication primitives come in play. There are three common approaches used in modern frameworks: MPI-collectives, task-based and computation-graph based...


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