Data Science Weekly Newsletter - Issue 214

Issue #214

Dec 28 2017

Editor Picks
  • A non-comprehensive list of awesome things other people did in 2017
    Editor’s note: For the last few years I have made a list of awesome things that other people did (2016,2015, 2014, 2013). Like in previous years I’m making a list, again right off the top of my head. If you know of some, you should make your own list or add it to the comments!...
  • Deep Learning Hardware Limbo
    With the release of the Titan V, we now entered deep learning hardware limbo. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. So for consumers, I cannot recommend buying any hardware right now. The most prudent choice is to wait until the hardware limbo passes. This might take as little as 3 months or as long as 9 months. So why did we enter deep learning hardware limbo just now?...

A Message from this week's Sponsor:


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Data Science Articles & Videos

  • 2017: The Year in Color
    Looking back on 2017, what did our world look like in color? We analyzed over 30 million English-language news documents to find out...
  • Fair and Balanced? Thoughts on Bias in Probabilistic Modeling
    In recent months and years, the Machine Learning community has conducting a notable amount of soul searching on the question of algorithmic bias: are our algorithms operating in ways that are fundamentally unfair towards specific groups within society?...
  • Episode #144: Machine Learning at the Large Hadron Collider
    We all know Python is becoming increasingly important in both science and machine learning. This week we journey to the very forefront of Physics. You will meet Michela Paganini, Michael Kagan, and Matthew Feickert. They all work at the Large Hadron Collider and are using Python and machine learning to help make the next major discovery in Physics...
  • The Case for B-Tree Index Structures
    Now I am all in favor of trying out new ideas, and adapting to the data distribution is clearly a good idea, but do we really need a neural network for that? Because, after all, the neuronal network is just an approximation of the CDF function. There are many other ways to approximate a function, for example spline interpolation: We define a few knots of the spline, and then interpolate between the knots...
  • Baby steps with CNTK and F#
    So what have I been up to lately? Obsessing over CNTK, the Microsoft deep-learning library. Specifically, the team released a .NET API, which got me interested in exploring how usable this would be from the F# scripting environment. I started a repository to try out some ideas already, but, before diving into that in later posts, I figure I could start by a simple introduction, to set some context...


  • Data Science & Analytics Associate - PepsiCo eCommerce - NYC

    eCommerce is one of the fastest-growing areas within the consumer products industry and represents a significant opportunity to accelerate growth for PepsiCo going forward.

    To ensure we win in this space we have established a dedicated eCommerce group, bringing together world class talent across F&B, digital, and key customers. While tied closely to broader PepsiCo, the eCommerce group has a unique start-up feel and defined values that embrace a more entrepreneurial mindset: bias for action; results oriented; community-focused; prioritization of people.

    In order to maintain the necessary pace to meet the growth targets and compete effectively against start-ups or technology competitors requires a step-change in our thinking and traditional approaches to data analytics and utilization. Accordingly, we are seeking a Data Scientist to manage data entry and integrity, data clean-up, query-based analysis, and project management with business users...

Training & Resources

  • Get A TensorFlow Tensor By Name
    Learn how to get A TensorFlow Tensor by name by using the TensorFlow get_default_graph operation and then the TensorFlow get_tensor_by_name operation... Video screencast and associated full written transcript tutorial...
  • Tutorial on Deep Generative Models
    At the end of this tutorial, audience member will have a full understanding of the latest advances in generative modelling covering three of the active types of models: Markov models, latent variable models and implicit models, and how these models can be scaled to high-dimensional data...



  • 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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