Data Science Weekly Newsletter - Issue 216

Issue #216

Jan 11 2018

Editor Picks
 
  • This Cat Sensed Death. What if Computers Could, Too?
    Of the many small humiliations heaped on a young oncologist in his final year of fellowship, perhaps this one carried the oddest bite: A 2-year-old black-and-white cat named Oscar was apparently better than most doctors at predicting when a terminally ill patient was about to die...
  • Google and Others Are Building AI Systems That Doubt Themselves
    The most powerful approach in AI, deep learning, is gaining a new capability: a sense of uncertainty. Researchers at Uber and Google are working on modifications to the two most popular deep-learning frameworks that will enable them to handle probability. This will provide a way for the smartest AI programs to measure their confidence in a prediction or a decision—essentially, to know when they should doubt themselves...
 
 

A Message from this week's Sponsor:

 

 
Do you wish you had a development environment tailored to Data Science?

SherlockML empowers hundreds of Data Scientists by managing their projects, infrastructure, packages and deployment. It is a platform designed to support the workflow all the way from data ingestion to a deployed model.

What’s the result? Iteration cycles that are weeks rather than months. No need to worry about the tech stack. More time spent doing science, collaboratively.

Contact ben.g@sherlockml.com for more information.

   

 

Data Science Articles & Videos

 
  • The 3 Tricks That Made AlphaGo Zero Work
    In this essay, I’ll try to give an intuitive idea of the techniques AlphaGo Zero used, what made them work, and what the implications for future AI research are. Let’s start with the general approach that both AlphaGo and AlphaGo Zero took to playing Go...
  • Identifying churn drivers with Random Forests
    My new project, called RetainKit, utilizes product usage and customer data to predict which customers might be leaving soon, and why. In this blog post, I’m going to outline some of the techniques we use in RetainKit to analyze the “Why” of churn...
  • Introduction to Deep Learning Trading in Hedge Funds
    One of the more attractive applications of deep learning is in hedge funds. Hedge funds are investment funds, financial organizations that raise funds from investors and manage them. They usually work with time series data and try to make some predictions. There is a special type of deep learning architecture that is suitable for time series analysis: recurrent neural networks (RNNs), or even more specifically, a special type of recurrent neural network: long short-term memory (LSTM) networks...
  • Leave A.I. Alone
    This push for broad legislation to regulate A.I. is premature. This is not, of course, to suggest that artificial intelligence should never be regulated. But if the past is any guide, treating it as a collection of separate technologies, in separate sectors, is destined to be the most effective way to control the benefits it creates — and the dangers it poses...
  • Neural Speed Reading via Skim-RNN
    Inspired by the principles of speed reading, we introduce Skim-RNN, a recurrent neural network (RNN) that dynamically decides to update only a small fraction of the hidden state for relatively unimportant input tokens...

 

Jobs

 
  • Senior Data Scientist - NBA - New York
    The Senior Data Scientist, Data Intelligence will be a key member of a new, growing function called Customer Data Strategy focused on building a foundational, world class analytical capabilities at the NBA. This person will report into the Associate Vice President of Data Intelligence, part of the enterprise global Marketing division.

    He/she will be part of an inaugural team of peers, that includes Data Strategy and Fan Engagement, creating advanced analytics solutions that help the NBA to grow engagement with our fans. The role leverages many different but related data streams that inform a single view of the fan across the NBA's global touchpoints including both on and offline platforms and channels...
 
 

Training & Resources

 
  • Reinforcement Learning Coach by Intel
    Intel is announcing the release of our Reinforcement Learning Coach — an open source research framework for training and evaluating reinforcement learning (RL) agents by harnessing the power of multi-core CPU processing to achieve state-of-the-art results...
  • SHAP (SHapley Additive exPlanations)
    Explains the output of any machine learning model using expectations and Shapley values. SHAP unifies aspects of several previous methods [1-7] and represents the only possible consistent and locally accurate additive feature attribution method based on expectations...
 
 

Books

 

  • Concrete Mathematics: A Foundation for Computer Science

    An insightful, revealing history of how mathematics transformed our world...

    "This is fun stuff. It's an interesting take on discrete math. In fact, it's really not discrete math; in includes discrete math but also includes other elements. I think this is especially good for the CS people, which is actually the intended audience..."


    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page.
 
 
P.S., Want to reach our audience / fellow readers? Consider sponsoring - grab a spot now; first come first served! All the best, Hannah & Sebastian
 
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