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Data Science Weekly Newsletter
January 10, 2019

Editor's Picks

  • Why you shouldn’t be a data science generalist
    I work at a data science mentorship startup, and I’ve found there’s a single piece of advice that I catch myself giving over and over again to aspiring mentees. And it’s really not what I would have expected it to be. Rather than suggesting a new library or tool, or some resume hack, I find myself recommending that they first think about what kind of data scientist they want to be...
  • Tensor Considered Harmful
    Despite its ubiquity in deep learning, Tensor is broken. It forces bad habits such as exposing private dimensions, broadcasting based on absolute position, and keeping type information in documentation. This post presents a proof-of-concept of an alternative approach, named tensors, with named dimensions...
  • Ask HN: Does ML research ever get translated to industry?
    It seems like industry doesn’t really use any of the recent advancements that come fresh from research, including things like capsule networks or advances in Neural Architecture search? Is there a gulf between the release of cutting edge research and its commercialization? Couldn’t many of these models be commercialized faster and be made available as enterprise products?...

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Data Science Articles & Videos

  • Designing an audio adblock
    Few people enjoy listening to ads on the radio. I built to enable listeners to skip ads interruptions on their favorite webradios. The algorithm is open source and this article describes how it works...
  • ESRGAN is pretty damn amazing - trying Max Payne with it
    In case you don't know what ESRGAN is it stands for Enhanced Super Resolution Generative Adverserial Networks's it's a AI technique that improves older games textures. Someone made a Max Payne HD pack using it and I wanted to see how much a difference it made I took some screenshots and I have to say WOW. This will make old games really shine...
  • FML: Face Model Learning from Videos
    We propose multi-frame video-based self-supervised training of a deep network that (i) learns a face identity model both in shape and appearance while (ii) jointly learning to reconstruct 3D faces. Our face model is learned using only corpora of in-the-wild video clips collected from the Internet...
  • A Guide to Deep Learning in Healthcare
    Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed...
  • Nintendo Learning Environment
    Together with a colleague of mine (Alexis Asseman) running some larger experiments on Nintendo; some early interesting results using distributed neuro-evolution - the solutions look very creative compared to other methods...
  • What They Don’t Teach You in Machine Learning Courses
    While interviewing hundreds of candidates, we’ve realised that even those with a strong technical background were very often lacking some essential skills. In this article, we’re talking about things that they don’t teach you in Machine Learning courses...


  • Data Scientist, Retention - Disney Streaming Services - NYC

    The Data Scientist is a critical position within DSS and in the Data organization who specializes in applying machine learning methods to meet optimization, personalization, recommendations and efficiency related challenges, in close collaboration with engineering and business partners. In this role, you will build and apply machine learning techniques and modern statistics to data both augment decision-making but to also significantly improve operational process problems through automation. You will collaborate across teams to define problems and develop automated solutions with the Data, Product and Engineering teams to be built into our products...

Training & Resources


  • Math for Machine Learning:
    Open Doors to Data Science and Artificial Intelligence

    From self-driving cars and recommender systems to speech and face recognition, machine learning is the way of the future. Would you like to learn the mathematics behind machine learning to enter the exciting fields of data science and artificial intelligence? There aren't many resources out there that give simple detailed examples and that walk you through the topics step by step.
    This book not only explains what kind of math is involved and the confusing notation, it also introduces you directly to the foundational topics in machine learning. This book will get you started in machine learning in a smooth and natural way, preparing you for more advanced topics and dispelling the belief that machine learning is complicated, difficult, and intimidating.
    Praise from students
    "Your book is by far the best I’ve found for understanding the derivations of machine learning algorithms. I love that you don’t skip steps and that you provide clear examples."--Robert H"... Link to preview of first 2 chapters and table of contents available here<

    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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