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Data Science Weekly Newsletter
Issue
104
November 19, 2015

Editor's Picks



A Message From This Week's Sponsor


  • Distribute Processing on Your Cluster with Anaconda
    Using Python on distributed computing technologies like Hadoop and Spark makes it easier to create and deploy advanced analytics in production. But managing packages on your cluster can be a full-time job. And that's why we created the cluster features of Anaconda. Learn how to manage Python packages across an entire cluster with one line of code. Watch the Recording & Get the Slides


Data Science Articles & Videos

  • Short Story on AI: A Cognitive Discontinuity
    The idea of writing a collection of short stories has been on my mind for a while. This post is my first ever half-serious attempt at a story, and what better way to kick things off than with a story on AI and what that might look like if you extrapolate our current technology...
  • Introducing a new way to visually search on Pinterest
    Discovery products at Pinterest are built on top of Pins. Last year, we introduced Guided Search, a feature built on top of understanding Pins’ descriptions. Before that, we launched Related Pins, a service built on top of understanding Pin to board connections. Though we’ve been able to use these Pinner curated signals to build new products and features, there’s one signal within every Pin we haven’t been able to utilize, a Pin’s image - until now...
  • Deep Learning for Visual Question Answering
    In this blog post, I’ll talk about the Visual Question Answering problem, and I’ll also present neural network based approaches for same. The source code for this blog post is written in Python and Keras, and is available on Github...
  • “Shrinking bull’s-eye” algorithm speeds up complex modeling
    Now MIT researchers have developed a new algorithm that vastly reduces the computation of virtually any computational model. The algorithm may be thought of as a shrinking bull’s-eye that, over several runs of a model, and in combination with some relevant data points, incrementally narrows in on its target: a probability distribution of values for each unknown parameter...
  • Machine learning could solve riddles of galaxy formation
    A new, faster modeling technique for galaxy formation has been developed by University of Illinois student Harshil Kamdar and professor Robert Brunner. The technique uses machine learning to cut down computing times from thousands of computing hours to mere minutes...
  • TensorFlow vs. Theano vs. Torch
    In this study, I evaluate some popular deep learning frameworks. The candidates are listed in alphabet order: TensorFlow, Theano, and Torch. This is a dynamic document and the evaluation is based the current state of their code, not what the authors claim in white papers...
  • Generating Faces with Torch
    In this blog post we'll implement a generative image model that converts random noise into images of faces! Code available on Github...



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