Data Science Weekly Newsletter - Issue 30

Issue #30

June 19 2014

Editor Picks

  • Architecting a Machine Learning System for Risk (Airbnb)

    Different risk vectors can require different architectures. For example, some risk vectors are not time critical, but require computationally intensive techniques to detect. An offline architecture is best suited for this kind of detection. For the purposes of this post, we are focusing on risks requiring realtime or near-realtime action. From a broad perspective, a machine-learning pipeline for these kinds of risk must balance two important goals...
  • Optimizing the Netflix Streaming Experience with Data Science

    Netflix is committed to delivering outstanding streaming service and is investing heavily in advancing the state of the art in adaptive streaming algorithms and network technologies such as Open Connect to optimize streaming quality. To put even more focus on "streaming science," we've created a new team at Netflix that's working on innovative approaches for using our data to improve QoE. In this post, I will briefly outline the types of problems we're solving...

Data Science Articles & Videos

  • World Cup Learning
    An IPython notebook using pybrain to learn/predict World cup outcomes...
  • Why I switched to Julia
    The following story, which I originally posted to The COBE Blog, explains why I began programming in Julia. Since then, I have found that Julia improves the performance of my other econometric estimators...
  • First, Second Derivative, Convolution and Quadratic Fitting via MCMC
    In this post: First, how we approach fitting a curve to a perfect quadratic function, using first order and second order derivatives of the function. Second, how one can do curve fitting in a quadratic function via Monte Carlo Markov Chain(MCMC) via Pymc. Last, how convolution could be used for numerical differentiation to estimate the coefficients of quadratic function...
  • Be the first to try Microsoft's new Machine Learning service
    With machine learning, computers can approach human performance in perception and understanding across vast amounts of data. Expensive and disconnected tools stood in the way of this innovation, but today Microsoft is democratizing machine learning...


  • Director of Analytics - Coursera - Mountain View, CA

    Coursera is focused on creating universal access to the world’s best University education. In less than two years we’ve brought over 500 courses to more than 6 million students worldwide, and we’re just getting started. Data Science is at the core of how we are achieving this mission - not surprising given that our founders are both pre-eminent Stanford professors in Machine Learning. Coursera is looking for a leader for our growing analytics organization...

Training & Resources

  • Deep Neural Networks: A Getting Started Tutorial
    Deep Neural Networks are the more computationally powerful cousins to regular neural networks. Learn exactly what DNNs are and why they are the hottest topic in machine learning research...



  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction

    Not new, though one of the most comprehensive books in the space...

    "The good news is, this is pretty much the most important book you are going to read in the space. It will tie everything together for you in a way that I haven't seen any other book attempt. "

    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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