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Data Science Weekly Newsletter
August 8, 2019

Editor's Picks

  • AI could be your wingman—er, wingbot—on your next first date
    The art of matchmaking has traditionally been the province of grandmas and best friends, parents, and even—sometimes—complete strangers. Recently they’ve been replaced by swipes and algorithms in an effort to automate the search for love. But Kevin Teman wants to take things one step further. The Denver-based founder of a startup called AIMM has built an app that matches prospective partners using just what they say to a British-accented AI....
  • Trends in Natural Language Processing: ACL 2019 In Review
    This week I had the great fortune to attend the Annual Meeting of the Association for Computational Linguistics (ACL) 2019 held in wonderful Florence in an old Medici family fortress. In this post, I want to distill some of the key learnings and trends I have gathered from a week spent with the NLP community, what the state of the field is in 2019 and where it is heading. When appropriate, I will reference papers highlighting some of these trends...
  • That Vexing Math Equation? Here’s an Addition
    A math equation recently stirred up trouble by seeming to offer two equally valid, and very different, solutions. Some software programs flatly refused to take the bait. My followup piece on 8÷2(2 + 2), in which I try to clear up the lingering disagreements but will probably only end up agitating the hornets’ nest again...

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Data Science Articles & Videos

  • An Interactive, Automated 3D Reconstruction of a Fly Brain
    Today, in collaboration with the Howard Hughes Medical Institute (HHMI) Janelia Research Campus and Cambridge University, we are excited to publish “Automated Reconstruction of a Serial-Section EM Drosophila Brain with Flood-Filling Networks and Local Realignment”, a new research paper that presents the automated reconstruction of an entire fruit fly brain...
  • Building Data Pipelines with Teraport
    Data pipelines are where most of the time is spent for those working with data because the bulk of a machine learning project involves data collection and cleaning. Loominus gives everyone the power to build the data pipelines critical to any machine learning project. Teraport is a powerful tool within the Loominus product suite that ingests and stages data. In another post, we’ll discuss the data ingestion APIs. For now we’ll focus on building a powerful data pipeline for feature engineering...
  • Adventures of a TensorFlow.js n00b: Part II: The Machine Trains Me
    Taking advantage of the fact that I am a writer in residence at Google embedded in a machine learning (ML) research group staffed by extremely patient developers, I’m learning how to write an ML app using TensorFlow.js, which happens to be one of the group’s projects. ...
  • Neural Blind Deconvolution Using Deep Priors
    Motivated by deep image prior (DIP), we in this paper present two generative networks for respectively modeling the deep priors of clean image and blur kernel, and propose an unconstrained neural optimization solution to blind deconvolution (SelfDeblur). Experimental results show that our SelfDeblur can achieve notable quantitative gains as well as more visually plausible deblurring results in comparison to state-of-the-art blind deconvolution methods on benchmark datasets and real-world blurry images...
  • Jeff Clune, Uber AI Labs - Presenting POET
    Video of a talk on POET (some of our work I am most excited about!): algorithms that create their own challenges and solve them in an endless, open-ended stream of learning and innovation...
  • Listening to the neural network gradient norms during training
    For this experiment, I made a very simple example showing a synthesized sound that was made using the gradient norm of each layer and for step of the training for a convolutional neural network training on MNIST using different settings such as different learning rates, optimizers, momentum...


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  • Data Scientist, Forecasting - Spotify - NYC

    We seek an exceptional Data Scientist to join our Forecasting team in New York. This individual will contribute to the development of cutting-edge models to predict Spotify’s future user growth and content consumption. The output of your models will serve as the basis for the company’s financial forecast as well as provide context for business performance to both internal and external stakeholders. Your work will also help the team create a time series forecasting infrastructure that can be leveraged throughout the company. Above all, you will be at the nexus of data science and business at one of the most innovative companies in the world...
        Want to post a job here? Email us for details >>

Training & Resources

  • Keras RNN API
    A recent addition to the tf.keras guide: a guide about RNN features, including performance considerations...
  • Scikit-learn finally introduced an API for plotting!
    Scikit-learn defines a simple API for creating visualizations for machine learning. The key feature of this API is to allow for quick plotting and visual adjustments without recalculation. In the following example, we plot a ROC curve for a fitted support vector machine...
  • Stationarity in time series analysis
    This post is meant to provide a concise but comprehensive overview of the concept of stationarity and of the different types of stationarity defined in academic literature dealing with time series analysis...


  • Python Crash Course: A Hands-On, Project-Based Introduction to Programming Thorough introduction to programming with Python...
    "I have read multiple beginner guides to Python. I am currently up to chapter 11 in Python Crash Course. So far this is far and away my favorite Python programming book..."...
    For a detailed list of books covering Data Science, Machine Learning, AI and associated programming languages check out our resources page
    P.S., Enjoy the newsletter? Please forward it to your friends and colleagues - we'd love to have them onboard :) All the best, Hannah & Sebastian

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