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Data Science Weekly Newsletter
September 21, 2017

Editor's Picks

  • Introducing: Unity Machine Learning Agents
    As the world’s most popular creation engine, Unity is at the crossroads between machine learning and gaming. It is critical to our mission to enable machine learning researchers with the most powerful training scenarios, and for us to give back to the gaming community by enabling them to utilize the latest machine learning technologies. As the first step in this endeavor, we are excited to introduce Unity Machine Learning Agents...

A Message From This Week's Sponsor

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On 9/27, join Metis for a free, online event featuring 25+ incredible speakers from the data science field. Speakers will demystify data science and discuss the training, tools, and career path to one of the world's hottest jobs. Every registrant gets access to bonus material from some of the industry's most influential thought-leaders. Secure your spot today! #DemistifyDS

Data Science Articles & Videos

  • Predicting NFL Plays with the xgboost Decision Tree Algorithm
    In all levels of football, on-field trends are typically discerned exclusively through voluminous film study of opponent history, and decisions are made using anecdotal evidence and gut instinct. These methods in isolation are highly inefficient and prone to human error. Enter – the play predictor. This tool aims to enhance in-game NFL decision making with a tool capable of predicting the type of play the opposing team will run at high accuracy in real-time...
  • Kullback-Leibler Divergence Explained
    In this post we're going to take a look at way of comparing two probability distributions called Kullback-Leibler Divergence (often shortened to just KL divergence). Very often in Probability and Statistics we'll replace observed data or a complex distributions with a simpler, approximating distribution. KL Divergence helps us to measure just how much information we lose when we choose an approximation....
  • Learning to Optimize with Reinforcement Learning
    Yet, there is a paradox in the current paradigm: the algorithms that power machine learning are still designed manually. This raises a natural question: can we learn these algorithms instead? This could open up exciting possibilities: we could find new algorithms that perform better than manually designed algorithms, which could in turn improve learning capability...
  • Supporting Hypothesis
    In September, Stripe is supporting the development of Hypothesis, an open-source testing library for Python created by David MacIver. Hypothesis is the only project we’ve found that provides effective tooling for testing code for machine learning, a domain in which testing and correctness are notoriously difficult...
  • Visualizing Distributions
    Many charting taxonomies include distributions, but they only present a few options. Let’s remedy that with a post on the many. We’ll use a single (completely fake) data set so we can easily compare how each chart type displays the same data...


  • Data Analyst - Glossier - New York, NY
    Glossier is looking for a Senior Data Analyst to take our data practice to the next level. You will work closely with our Head of Data to provide data-driven insights to teams across the organization in order to inform strategic decision-making. You will take a leading role in shaping our Data practices, and you will use your insights to scope projects, propose approaches, and help to drive them to completion. If you enjoy finding the signal in the noise, bringing order and structure to inefficiencies, and know the mean time and standard deviation of your commute then please apply...

Training & Resources

  • Creating a Bar Chart
    The very basics of how to create a bar chart or stacked bar chart with labels and an axis in Semiotic...
  • Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
    This is a PyTorch version of fairseq, a sequence-to-sequence learning toolkit from Facebook AI Research. The original authors of this reimplementation are (in no particular order) Sergey Edunov, Myle Ott, and Sam Gross. The toolkit implements the fully convolutional model described in Convolutional Sequence to Sequence Learning and features multi-GPU training on a single machine as well as fast beam search generation on both CPU and GPU. We provide pre-trained models for English to French and English to German translation...


Reminder, if you enjoyed the first 200 newsletters and want many more ... Please make a donation to help keep us going :) - All the best, Hannah & Sebastian

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