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Data Science Weekly Newsletter
October 19, 2017

Editor's Picks

  • Google's Learning Software Learns to Write Software
    Artificial-intelligence researchers at Google are trying to automate the tasks of highly paid workers more likely to wear a hoodie than a coat and tie—themselves. In a project called AutoML, Google’s researchers have taught machine-learning software to build machine-learning software...
  • Your Brain Limits You to Just Five BFFs
    The number of people we can have meaningful contact with is limited by the size of our brains. Now this group seems to be subdivided into layers, say anthropologists...
  • Generalization in Deep Learning
    This paper explains why deep learning can generalize well, despite large capacity and possible algorithmic instability, nonrobustness, and sharp minima, effectively addressing an open problem in the literature...

A Message From This Week's Sponsor

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Data Science Articles & Videos

  • Advice For New and Junior Data Scientists
    Two years ago, I shared my experience on doing data science in the industry. The writing was originally meant to be a private reflection for myself to celebrate my two year twitterversary at Twitter, but I instead published it on Medium because I believe it could be very useful for many aspiring data scientists. Fast forward to 2017, I have been working at Airbnb for a little bit less than two years and have recently become a senior data scientist — an industry title used to signal that one has acquired a certain level of technical expertise. As I reflect on my journey so far and imagine what’s next to come, I once again wrote down a few lessons that I wish I had known in the early days of my career....
  • Coloring B&W portraits with neural networks.
    Earlier this year, Amir Avni used neural networks to troll the subreddit /r/Colorization - a community where people colorize historical black and white images manually using Photoshop. They were astonished with Amir’s deep learning bot - what could take up to a month of manual labour could now be done in just a few seconds. I was fascinated by Amir’s neural network, so I reproduced it and documented the process. First off, let’s look at some of the results/failures from my experiments...
  • Neural Networks for Advertisers
    Recently I came across a problem to solve using some sort of machine learning capabilities, which was the need to count the total time during which a specific company was advertised on the various places at a football match...
  • Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image
    We propose a unified formulation for the problem of 3D human pose estimation from a single raw RGB image that reasons jointly about 2D joint estimation and 3D pose reconstruction to improve both tasks. We take an integrated approach that fuses probabilistic knowledge of 3D human pose with a multi-stage CNN architecture and uses the knowledge of plausible 3D landmark locations to refine the search for better 2D locations. The entire process is trained end-to-end, is extremely efficient and obtains state-of-the-art results on Human3.6M outperforming previous approaches both on 2D and 3D errors...


  • Data Scientist - MealPal - New York
    Are you passionate about helping an organization make smart decisions in order to deliver the best product and user experience? Do you want to join a fast-paced, growing company? As a Data Scientist at MealPal, you will focus on using data to drive business strategy and take our company to the next level. You will have the opportunity to think critically and problem solve in order to drive valuable and executable insights...

Training & Resources

  • Streaming Dataframes
    This post describes a prototype project to handle continuous data sources of tabular data using Pandas and Streamz...


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