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Data Science Weekly Newsletter
June 7, 2018

Editor's Picks

  • Maps of the Brain
    A century of cortical cartography has helped neurologists understand how the human brain perceives and interprets our complex surroundings...
  • Human-Machine Work Teams
    “As the future of work spans from lightly technology-augmented employees all the way up to full job replacement by robots, the workforce will increasingly become a hybrid of humans and machines,” writes Gownder in conclusion. “Human-machine teaming… will grow into a key workforce technology discipline.” Companies should keep in mind that...
  • Understanding Deep Learning for Object Detection
    In this blog post, I will explore important work in deep learning for object detection. I will introduce how those methods evolved over time and compare their differences and similarities. By showing innovations and limitations of each method and how later methods progress to solve previous problems, you will see — which is quite interesting — that research gets improved each time by just a small but elegant adjustment...

A Message From This Week's Sponsor

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Data Science Articles & Videos

  • Data Science Leaders: There are too many of you
    Today I spoke at a summit for leaders in data science (the discipline that spans machine learning, artificial intelligence, statistics, data summarization, and visualization). As I looked over at the sea of faces belonging to the brightest trailblazers guiding today’s data science teams, I found myself thinking, “There are too many of you.”...
  • Starting a Second Machine Learning Tools Company, Ten Years Later
    I’ve spent the last six months heads down building a new machine learning tool called Weights and Biases with my longtime cofounder Chris Van Pelt, my new cofounder and friend Shawn Lewis and brave early users at Open AI, Toyota Research, Uber and others. Now that it’s public I wanted to talk a little bit about why I’m (still) so excited about building machine learning tools...
  • Relational inductive biases, deep learning, and graph networks
    The following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective...
  • DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks
    Non-contact video-based physiological measurement has many applications in health care and human-computer interaction. Practical applications require measurements to be accurate even in the presence of large head rotations. We propose the first end-to-end system for video-based measurement of heart and breathing rate using a deep convolutional network...


  • Data Scientist - SQAD - NYC
    SQAD LLC is on the cutting edge of digital and traditional, media cost measurement and forecasting. Recognized as an industry pioneer, SQAD’s data and systems serve the some of the biggest brands in media. SQAD provides reliable media cost data to advertising agencies, media buying companies, advertisers, television and radio stations, cable companies, program syndicators and Internet publishers.
    What you’ll do: You will be part of a talented Dev-Ops team that is responsible for designing and developing cloud-based data solutions utilizing best-of-breed open source tools and data technologies...

Training & Resources

  • Launch TensorFlow TensorBoard
    Learn how to use the TensorBoard command line utility to launch the TensorFlow TensorBoard web service, via a screencast video and full tutorial transcript...
  • Improving Deep Learning Performance with AutoAugment
    AutoAugment is an automatic way to design custom data augmentation policies for computer vision datasets, e.g., guiding the selection of basic image transformation operations, such as flipping an image horizontally/vertically, rotating an image, changing the color of an image, etc...


  • Test-Driven Machine Learning
    The book begins with an introduction to test-driven machine learning and quantifying model quality. From there, you will test a neural network, predict values with regression, and build upon regression techniques with logistic regression...

    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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