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Data Science Weekly Newsletter
December 7, 2017

Editor's Picks

  • Optimization for Deep Learning Highlights in 2017
    In this blog post, I will touch on the most exciting highlights and most promising directions in optimization for Deep Learning in my opinion. Note that this blog post assumes a familiarity with SGD and with adaptive learning rate methods such as Adam...

A Message From This Week's Sponsor

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Data Science Articles & Videos

  • Innovating Faster on Personalization Algorithms at Netflix Using Interleaving
    To accelerate the pace of algorithm innovation, we have devised a two-stage online experimentation process. The first stage is a fast pruning step in which we identify the most promising ranking algorithms from a large initial set of ideas. The second stage is a traditional A/B test on the pared-down set of algorithms to measure their impact on longer-term member behavior. In this blog post, we focus on our approach to the first stage: an interleaving technique that unlocks our ability to more precisely measure member preferences...
  • Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
    The game of chess is the most widely-studied domain in the history of artificial intelligence. The strongest programs are based on a combination of sophisticated search techniques, domain-specific adaptations, and handcrafted evaluation functions that have been refined by human experts over several decades. In contrast, the AlphaGo Zero program recently achieved superhuman performance in the game of Go, by tabula rasa reinforcement learning from games of self-play. In this paper, we generalise this approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains...
  • Stakeholder-Driven Data Science at Warby Parker
    In this Data Science Popup Session, Max Shron, the head of data science at Warby Parker, delves into stakeholder-driven data science. His approach enables his data science team to work on the right kinds of things, deliver as much value as possible, and be seen as a driver of value across the company...
  • Monte Carlo Simulation with Categorical Values
    I ran into a situation where I was gathering some data with some level of imperfection. My stakeholder wanted to know what the impact of that imperfection on the important metrics would be. I could have made a guess, but instead I turned to the data. Initially, I thought to calculate the best case and worst case scenarios. This idea is useful in that it gives you a range on what you don't know, but it's also beneficial to know how likely each of those scenarios (and things in between) are. That's where Monte Carlo simulation comes in handy...
  • Speed Kills: How Much does a Slow Web Site Cost?
    Here's a quick experiment using one of my favorite data science toolkits: SciPy and Jupyter. By downloading the page load times from Google Analytics, and comparing it to the conversion rate (how often people buy stuff), it's possible to place an actual dollar value on page speed, unique to your audience...
  • Designing An Analytics Stack Like We Design Software
    Since analytics needs are highly variable across teams and undergo frequent evolution within teams, this article will not attempt to provide guidance on choosing the tools that are right for you. Instead, we’ll shift our focus to understanding the cause of the trends we’re observing, and how embracing this evolution and leveraging its benefits may serve as the catalyst to take our analytical capabilities to the next level...


  • Data Scientist - Farfetch - NYC
    As a fast-growing fashion e-commerce business and one of the world’s most valuable startups, harnessing the value of the data generated by our operations is critical to Farfetch’s future success and the Data Science teams are at the forefront of this effort.
    All of our work in Data Science is directed at building software solutions that enhance the marketing activity of the company by using Machine Learning and advanced statistical methods. This means understanding the customer, figuring out who they are, what they want and how to get their attention. Critically, we build systems that do this autonomously...

Training & Resources

  • Machine Learning 101 deck
    Getting started with ML? Want a deeper understanding, or maybe just plain confused? This deck is a collection of knowledge I gathered over 2 years of reading many many articles so you don't have to...


  • The Lady Tasting Tea:
    How Statistics Revolutionized Science in the Twentieth Century

    An insightful, revealing history of how mathematics transformed our world...
    "I have taken courses in statistics, taught it many times and solved several statistical problems that have appeared in journals. But until I read this book, I never really thought about it in so deep and philosophical a manner..."...
    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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