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Data Science Weekly Newsletter
April 24, 2014

Editor's Picks

  • Things I learned by implementing Convolutional Neural Nets
    Aside from the basics, I didn’t know much about neural nets in depth so I decided to teach myself. My philosophy is that the best way to learn something is by doing... ultimately I decided to implement my own simple neural net (or convolutional neural net to be exact, since I do have an inclination towards vision). Since implementation details rarely make into academic literature I decided to take notes about the practical challenges I ran into and share them with the world...
  • Data Science & Online Retail - at Warby Parker & Beyond
    We recently caught up with Carl Anderson, Director of Data Science at Warby Parker (and previously at One Kings Lane). We were keen to learn more about his background, his perspective on how data science is shaping the online retail landscape and what he is working on now at Warby Parker...
  • Deep Learning - How & Why Deep Learning Methods Work
    The recent resurrection of multi-layer neural networks is generating a lot of interest currently, with deep learning appearing on the New York Times front page, and big companies like Google and Facebook hunting for the experts in this field. Jürgen’s talk sheds more light on how deep learning methods work, and why they work...

Data Science Articles & Videos

  • Elusive Data Scientists Driving High Salaries
    Data scientists, the elusive kingpins in the Big Data movement, are earning base salaries of well over $200K, are younger, overwhelmingly male, have at least a master’s degree and probably a Ph.D., and one in three are foreign born, according to the first-ever study looking at salaries, education levels, gender and geographical location of this new profession...
  • DeepFace: Closing the Gap to Human-Level Performance in Face Verification
    In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network...
  • The Face Recognition Algorithm That Finally Outperforms Humans
    The best face recognition systems can beat human performance in ideal conditions. But their performance drops dramatically as conditions get worse. So computer scientists would dearly love to develop an algorithm that can take the crown in the most challenging conditions too. Today, Chaochao Lu and Xiaoou Tang at the Chinese University of Hong Kong say they’ve done just that. These guys have developed a face recognition algorithm called GaussianFace that outperforms humans for the first time...
  • Self-Learning Helicopter Uses Neural Network
    We developed a self learning 1 degree of freedom (DOF) helicopter using a neural network learning algorithm and infrared (IR) distance measurement. The primary goal is to increase the helicopter height to a desired level in the quickest amount of time and with the least amount of learning trials...
  • Automatic Weighting of Imbalanced Datasets
    Very often datasets are imbalanced. That is, the number of instances for each of the classes in the target variable that you want to predict is not proportional to the real importance of each class in your problem. In this post, we’ll see how you can deal with imbalanced datasets configuring your models or ensembles to use weights via BigML’s web interface...
  • New NYC bootcamp for Data Scientists:
    It’s free, but harder to get into than Harvard

    Finding a great data scientist can feel like searching for Princess Peach. She’s always in another castle. There are plenty of programmers who can match a startup’s pace. There are plenty of PhDs with solid research backgrounds. But there’s a serious dearth of job applicants equipped with both skill sets. Foursquare veteran Michael Li is working on a solution: a hacker bootcamp for data scientists. It’s called The Data Incubator...


  • Data Scientist, i-Tunes - Apple, Cupertino, CA
    The iTunes Engineering team has a proud tradition of delivering cutting-edge products in a competitive marketplace. We seek to maintain a challenging and rewarding environment where the best engineers and scientists can collaborate and produce real-world improvements in customers' online experience. Successful candidates will solve problems unique in scale and concept in the pursuit of new and original features...

Training & Resources

  • Patterns for research in Machine Learning
    Here I list a handful of code patterns that I wish I was more aware of when I started my PhD. Each on its own may seem pointless, but collectively they go a long way towards making the typical research workflow more efficient. And an efficient workflow makes it just that little bit easier to ask the research questions that matter...
  • Preparing for Insight
    When I was first considering making the transition from applied physics to data science, I had a lot of questions. What skills did I need to develop to get started in data science? What courses should I take? Did I need to know how to program and code? What languages? How much statistics did I need to know? The list goes on. Now that I've spent a few months as a Program Director here at Insight, I think it's time I shared with you the tools and tips that got me, and nearly 100 other Insight Fellows, started on our transition...


  • Data Science for Business:
    What you need to know about Data Mining and Data-Analytic Thinking

    Amazon bestseller on Data Mining (4.8 stars from 50+ reviews).
    "Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate the principles of Data Science"
    "When trying to learn about a new field, one of the most common difficulties is to find books (and other materials) that have the right "depth". All too often one ends up with either a friendly but largely useless book that oversimplifies or a heavy academic tome that, though authoritative and comprehensive, is condemned to sit gathering dust in one's shelves. "Data Science for Business" gets it just right."...

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