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Data Science Weekly Newsletter
November 16, 2017

Editor's Picks

  • Software 2.0
    I sometimes see people refer to neural networks as just “another tool in your machine learning toolbox”. They have some pros and cons, they work here or there, and sometimes you can use them to win Kaggle competitions. Unfortunately, this interpretation completely misses the forest for the trees. Neural networks are not just another classifier, they represent the beginning of a fundamental shift in how we write software. They are Software 2.0...
  • A Visual Guide to Evolution Strategies
    In this post I explain how evolution strategies (ES) work with the aid of a few visual examples. I try to keep the equations light, and I provide links to original articles if the reader wishes to understand more details...

A Message From This Week's Sponsor

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Data Science Articles & Videos

  • 10 Minutes of Imaginary Japanese Anime Face
    10 Minutes of Imaginary Japanese Anime Face, dreamed by (an 256x256 version) of our #MakeGirlsMoe GAN model that is accepted in #NIPS2017 Workshop for Machine Learning for Creativity and Design!...
  • Deep Learning is Eating Software
    When I had a drink with Andrej Karpathy a couple of weeks ago, we got to talking about where we thought machine learning was going over the next few years. Andrej threw out the phrase “Software 2.0”, and I was instantly jealous because it captured the process I see happening every day across hundreds of projects. I held my tongue until he got his blog post out there, but now I want to expand my thoughts on this too...
  • Gaussian Distributions are Soap Bubbles
    This post is just a quick note on some of the pitfalls we encounter when dealing with high-dimensional problems, even when working with something as simple as a Gaussian distribution...
  • Understanding Hinton’s Capsule Networks. Part I: Intuition.
    Last week, Geoffrey Hinton and his team published two papers that introduced a completely new type of neural network based on so-called capsules. In addition to that, the team published an algorithm, called dynamic routing between capsules, that allows to train such a network. In this post, I will explain why this new architecture is so important, as well as intuition behind it. In the following posts I will dive into technical details...


  • Machine Learning / AI Architect – Research & Development -
    Citrix - Patras, Greece

    Citrix is expanding its Advanced Analytics team, with seasoned professionals in the ML/AI/Data Science and Security domains. You will join a crack team, with years of history delivering high-quality Analytics products, and a global outreach. You will be collaborating with fellow engineering teams across the globe, on the cutting edge Citrix Analytics Service

    Key Responsibilities:
    • Research & develop Machine Learning models for security problems, in the areas of Networking, Application & Data.
    • Suggest, collect and synthesize requirements and create effective features.
    • Apply research methodologies to identify the Machine Learning models for the problem at hand

Training & Resources

  • On-Device Conversational Modeling with TensorFlow Lite
    Today, we announce TensorFlow Lite, TensorFlow’s lightweight solution for mobile and embedded devices. This framework is optimized for low-latency inference of machine learning models, with a focus on small memory footprint and fast performance...
  • Hosting an R Shiny Application on Amazon EC2
    In the second part of this three part series I will discuss how I host using a Shiny app hosted on an Amazon EC2 instance, also using Amazon’s Route 53 DNS service to setup a custom domain name...


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