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Data Science Weekly Newsletter
April 19, 2018

Editor's Picks

A Message From This Week's Sponsor

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Data Science Articles & Videos

  • Who's a Good AI? Dog-based data creates a canine machine learning system
    We’ve trained machine learning systems to identify objects, navigate streets and recognize facial expressions, but as difficult as they may be, they don’t even touch the level of sophistication required to simulate, for example, a dog. Well, this project aims to do just that — in a very limited way, of course...
  • Forecasting Uber Demand in NYC
    I decided to see if I could forecast hourly Uber demand across NYC neighborhoods. In addition to time-lagged features (such as previous week’s demand), I added information specific to each neighborhood to improve my predictions. As a final result, I obtained relatively accurate unique forecasts for all neighborhoods in NYC...
  • Paper Repro: Deep Neuroevolution
    In this post, we reproduce the recent Uber paper “Deep Neuroevolution: Genetic Algorithms are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning”, which amazingly showed that simple genetic algorithms sometimes performed better than apparently advanced reinforcement learning algorithms on well studied problems such as Atari games. We will ourselves reach state of the art performance on Frostbite)...
  • Text Embedding Models Contain Bias. Here's Why That Matters.
    Neural network models can be quite powerful, effectively helping to identify patterns and uncover structure in a variety of different tasks, from language translation to pathology to playing games. At the same time, neural models (as well as other kinds of machine learning models) can contain problematic biases in many forms...
  • Probabilistic Machine Learning in TensorFlow
    In this episode of Coffee with a Googler, Laurence Moroney sits down with Josh Dillon. Josh works on TensorFlow, Google’s open source library for numerical computation, which is typically used in Machine Learning and AI applications. He discusses working on the Distribution API, which is based on probabilistic programming. Watch this video to find out what exactly probabilistic programming is, where the use of Distributions and Bijectors comes into play, & how you can get started...
  • Ads That Click
    Classifying Ads using CATBoost Model based on the features of the ads and the user’s behavior. The objective of my project was to analyze user behavior and derive if they will like a particular ad in the future or not. The intent was to maximize the value of the advertiser at the same time improve the user experience...


  • Data Scientist - VillageCare - NYC
    VillageCare is a community-based, non-profit organization serving people with chronic care needs, as well as seniors and individuals in need of continuing care and rehabilitation services.
    The Data Scientist will support our Provider Relations team with insights gained from analyzing company data. The ideal candidate is adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action...

Training & Resources


  • Data Science from Scratch: First Principles with Python "It does three things superbly: covers the basic low level tools of a data scientist (the "from scratch" part), gives a great overview of useful Python programming examples for those new to Python, and gives an amazingly succinct yet high level overview of the mathematics and statistics required for data science..."...
    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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