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Data Science Weekly Newsletter
Issue
290
June 13, 2019

Editor's Picks

  • Weight Agnostic Neural Networks
    Inspired by precocial species in biology, we set out to search for neural net architectures that can already (sort of) perform various tasks even when they use random weight values...



A Message From This Week's Sponsor



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Data Science Articles & Videos

  • You can train an AI to fake UN speeches in just 13 hours
    Deep-learning techniques have made it easier and easier for anyone to forge convincing misinformation. But just how easy? Two researchers at Global Pulse, an initiative of the United Nations, decided to find out. In a new paper, they used only open-source tools and data to show how quickly they could get a fake UN speech generator up and running. ...
  • Amazon Personalize - a fully managed AI-powered recommendation service
    Amazon today announced the general availability of Amazon Personalize, an AWS service that facilitates the development of websites, mobile apps, and content management and email marketing systems that suggest products, provide tailored search results, and customize funnels on the fly...
  • Why Hadoop Failed and Where We Go from Here
    With Cloudera losing 42% market cap and MapR signaling they are about to close shop, this week left little doubt that Hadoop is on the way out. So, what happened? And where do we as an industry go from here?...
  • A Survey of Reinforcement Learning Informed by Natural Language
    To be successful in real-world tasks, Reinforcement Learning (RL) needs to exploit the compositional, relational, and hierarchical structure of the world, and learn to transfer it to the task at hand. We survey the state of the field, including work on instruction following, text games, and learning from textual domain knowledge...
  • Variational Pretraining for Semi-supervised Text Classification
    We introduce VAMPIRE, a lightweight pretraining framework for effective text classification when data and computing resources are limited. We pretrain a unigram document model as a variational autoencoder on in-domain, unlabeled data and use its internal states as features in a downstream classifier...
  • SHAP (SHapley Additive exPlanations)
    A unified approach to explain the output of any machine learning model. SHAP connects game theory with local explanations, uniting several previous methods [1-7] and representing the only possible consistent and locally accurate additive feature attribution method based on expectations...
  • 5 Ways to Leverage AI Research in Marketing and Advertising
    Marketing is evolving, and AI has solved a lot of marketing challenges we are facing. However, AI has developed rapidly that it becomes a challenge to follow all the research advances and possible business applications. In our article, we have focused on the 5 key challenges enterprise marketers face and how we can leverage recent AI breakthroughs...



Jobs

  • Data Scientist, Analytics - DoorDash - NYC

    The Analytics team is looking for Data Scientists to drive measurement, strategy, and tactical decision-making across the company. You’ll be solving problems that range from customer acquisition to balancing supply and demand to new city launches to marketplace efficiency. You’ll be designing and analyzing A/B tests for new product features, generating and sizing opportunities to prioritize new initiatives, and defining key performance metrics for the team. Data Scientists at DoorDash work cross-functionally to uncover insights and turn them into actionable recommendations...


Training & Resources

  • Transpose A Matrix In PyTorch
    Learn how to use transpose a matrix in PyTorch by using the PyTorch T operation, via a screencast video and full tutorial transcript...


Books


  • Guesstimation: Solving the World's Problems on the Back of a Cocktail Napkin
    "Guesstimation enables anyone with basic math and science skills to estimate virtually anything--quickly--using plausible assumptions and elementary arithmetic"...
    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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    P.S., Want to reach our audience / fellow readers? Consider sponsoring - grab a spot now; first come first served! All the best, Hannah & Sebastian

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