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Data Science Weekly Newsletter
August 31, 2017

Editor's Picks

  • My Year at Brain
    I've spent the past year doing the Google Brain Residency Program. It's been a great opportunity for the first year out of my PhD, and in this blog post I'll describe what the residency was like, what I worked on while here, and what I'm doing next....
  • Advice for non-traditional data scientists
    I am now a Senior R Developer at a survey company called When I started, I honestly didn’t have any particular skills or capacity which would have made data science a good career choice. I studied philosophy in undergrad, and while I had done a bit of statistics, it wasn’t something I would have said I was comfortable with. All I really had was an interest and the capacity to learn new things. If you’re in a similar boat, here is some advice about the process...

A Message From This Week's Sponsor

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Data Science Articles & Videos

  • How AI Fits Into Your Data Science Team
    In their HBR Big Idea feature, Erik Brynjolfsson and Andrew McAfee argue that AI and machine learning will soon become “general-purpose technologies,” as significant as electricity or the internal combustion engine. They represent a landmark change in our technical capabilities and will power the next wave of economic growth...
  • Background removal with deep learning
    Background removal is a task that is quite easy to do manually, or semi manually (Photoshop, and even Power Point has such tools) if you use some kind of a “marker” and edge detection, see here an example. However, fully automated background removal is quite a challenging task, and as far as we know, there is still no product that has satisfactory results with it, although some do try...
  • Machine Learning in Production:
    From trained models to prediction servers

    After days and nights of hard work, going from feature engineering to cross validation, you finally managed to reach the prediction score that you wanted. Is it over? Well, since you did a great job, you decided to create a microservice that is capable of making predictions on demand based on your trained model. Let’s figure out how to do it. This article will discuss different options and then will present the solution that we adopted at ContentSquare to build an architecture for a prediction server...


  • Senior Data Scientist - Trace3 - Irvine, CA
    Do you enjoy solving computer vision problems such as Optical Character Recognition (OCR), object detection and image classification? Do you love applying new state of the art machine learning and deep learning algorithms? If yes, consider joining Trace3. At Trace3, we bridge the gap between traditional IT and business stakeholders to partner with organizations to achieve success in the Big Data World. Trace3's best and brightest people make this happen. If you are someone who wants to move the needle in this industry, please apply...

Training & Resources

  • Object detection: an overview in the age of Deep Learning
    There’s no shortage of interesting problems in computer vision, from simple image classification to 3D-pose estimation. One of the problems we’re most interested in and have worked on a bunch is object detection. Like many other computer vision problems, there still isn’t an obvious or even “best” way to approach the problem, meaning there’s still much room for improvement. Before getting into object detection, let’s do a quick rundown of the most common problems in the field...


  • Thinking Statistically
    "A truly excellent read and far more fun than a book about statistics has any right to be..."...
    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page...

Looking to hire a Data Scientist? Find an awesome one among our readers! Email us for details on how to post your job :) - All the best, Hannah & Sebastian

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