Data Science Weekly Newsletter - Issue 295

Issue #295

July 18 2019

Editor Picks
   
 

A Message from this week's Sponsor:

 

 
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Data Science Articles & Videos

 
  • New neural net snakes
    There’s a kind of neural network that learns to imitate whatever text you give it, whether that’s recipes, song lyrics, or even the names of guinea pigs. Their imitations are often imperfect (they only know what’s in their dataset and therefore end up accidentally coming up with things that they don’t know are bad ideas). But one area where they tend to do well is inventing new species of things. The neural net’s birds were entirely believable, and its fish were generally no stranger than the species that already exist. So for my next project, I decided to generate some snakes...
  • Autocompletion with deep learning
    TabNine is an autocompleter that helps you write code faster. We’re adding a deep learning model which significantly improves suggestion quality. You can see videos below...
  • Musical examples from LakhNES: generating 8-bit music using Transformer
    This web repository contains musical examples from our paper LakhNES: Improving multi-instrumental music generation with cross-domain pre-training. LakhNES is a Transformer model which benefits from transfer learning: it is pre-trained on the Lakh MIDI dataset and fine-tuned on the NES-MDB 8-bit music dataset...
  • Efficient Video Generation on Complex Datasets
    Generative models of natural images have progressed towards high fidelity samples by the strong leveraging of scale. We attempt to carry this success to the field of video modeling by showing that large Generative Adversarial Networks trained on the complex Kinetics-600 dataset are able to produce video samples of substantially higher complexity than previous work. Our proposed network, Dual Video Discriminator GAN (DVD-GAN), scales to longer and higher resolution videos by leveraging a computationally efficient decomposition of its discriminator...
  • What tools do employers want data scientists to know?
    You find yourself overwhelmed with all the various tools mentioned in data science forums. You were hoping MATLAB would be enough, but you are inundated with advice of how to get started. You get advice to learn and practice R. Then you should learn Python. Then you should learn SQL...
 
 

Training*

 

 
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Jobs

 
  • Senior Data Scientist - TRANZACT - NJ or Raleigh, NC

    Tranzact is a fast paced, entrepreneurial company offering a well-rounded suite of marketing solutions to help insurance companies stay ahead of the competition. The Senior Data Scientist will be solving the toughest problems at Tranzact by using data. More specifically, responsible for gathering data, conducting analysis, building predictive algorithms and communicating findings to drive profitable growth and performance across Tranzact. Must have a strong grasp on the data structure, business needs, and statistical and predictive modeling. Minimum 7 years of experience building predictive algorithms...

        Want to post a job here? Email us for details >> team@datascienceweekly.org
 

 

Training & Resources

 
  • Robust Deep Learning Under Distribution Shift
    Just gave a talk on my recent line of work on "Robust Deep Learning Under Distribution Shift" at Simons Institute. Topics incl. impossibilities, label shift, detection, importance weighting & domain-adversarial methods...
  • Natural Adversarial Examples
    We introduce natural adversarial examples -- real-world, unmodified, and naturally occurring examples that cause classifier accuracy to significantly degrade...
 
 

Books

 

  • The Lady Tasting Tea:
    How Statistics Revolutionized Science in the Twentieth Century

    An insightful, revealing history of how mathematics transformed our world...

    "I have taken courses in statistics, taught it many times and solved several statistical problems that have appeared in journals. But until I read this book, I never really thought about it in so deep and philosophical a manner..."


    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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