Data Science Weekly Newsletter - Issue 314

Issue #314

Nov 28 2019

Editor Picks
 
  • Single Headed Attention RNN: Stop Thinking With Your Head
    The leading approaches in language modeling are all obsessed with TV shows of my youth - namely Transformers and Sesame Street. Transformers this, Transformers that, and over here a bonfire worth of GPU-TPU-neuromorphic wafer scale silicon. We opt for the lazy path of old and proven techniques with a fancy crypto inspired acronym: the Single Headed Attention RNN (SHA-RNN). The author's lone goal is to show that the entire field might have evolved a different direction if we had instead been obsessed with a slightly different acronym and slightly different result...
  • Diversity in AI is not your problem, it’s hers
    I came to a shocking conclusion while writing about diversity for my book on machine learning: diversity in Artificial Intelligence is not your problem, it’s hers. I mean, of course, that the problem is with the English pronoun “hers”...
 
 

A Message from this week's Sponsor:

 

 
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Data Science Articles & Videos


  • Alexa’s voice can now express disappointment and excitement
    You might hear a little more emotion from Alexa’s vaguely-robotic voice some time soon, as Amazon announced today that developers can now have Alexa respond to questions from US users with a “happy/excited” or a “disappointed/empathetic” tone...
  • Data Science is Boring (Part 1)
    Many people cherrypick the exciting parts of doing Data Science (or ML, Machine Learning) to motivate themselves and others. But we must face a reality: the real work is often “boring” — boring as comparing to what people romanticize. Feeling bored creates tension; it, ultimately, leads to a high turnover rate in data science. I want to share what I actually do and how I cope with the “boringness in data science”. I hope to help you, the aspiring Data Scientists, to set the right expectations. So, once you decide to pursue a Data Science career, you are in it for the long game. Enjoy...
  • AI will disrupt white-collar workers the most, predicts a new report
    Researchers looked at the text of AI patent and job descriptions, and quantified the overlap in order to identify the kinds of tasks and occupations likely to be affected. The analysis says that AI will be a significant factor in the future work lives of managers, supervisors, and analysts, shaking up all sorts of white-collar work from law firms, marketing roles, and publishers to computer programming...
  • MixNMatch: Multifactor Disentanglement and Encoding for Conditional Image Generation
    We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, for mix-and-match image generation. We build upon FineGAN, an unconditional generative model, to learn the desired disentanglement and image generator, and leverage adversarial joint image-code distribution matching to learn the latent factor encoders...
  • AI for Healthcare
    What happens when you bring physicians, surgeons, CEOs, CTOs, COOs, strategic directors, product managers, software engineers, data analysts, professors and researchers together to discuss the current state of artificial intelligence in healthcare? Well, that’s the idea behind Ai4 Healthcare. This is my experience at the conference...
 
 

Training*

 
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Jobs


  • Data Scientist - Driven Brands - Charlotte, NC

    The Data Scientist for Driven Brands focus will be responsible for providing reliable marketing, media and promotional performance analysis and reporting to Senior Executives and Business Unit Management to be used to make decisions impacting the performance of the business...

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

 

Training & Resources

  • RoboNet: A Dataset for Large-Scale Multi-Robot Learning
    Motivated by the success of large-scale data-driven learning, we created RoboNet, an extensible and diverse dataset of robot interaction collected across four different research labs. The collaborative nature of this work allows us to easily capture diverse data in various lab settings across a wide variety of objects, robotic hardware, and camera viewpoints. Finally, we find that pre-training on RoboNet offers substantial performance gains compared to training from scratch in entirely new environments....
 
 

Books

 

  • Data Visualization with Python and JavaScript:
    Scrape, Clean, Explore & Transform Your Data

    Learn how to turn raw data into rich, interactive web visualizations with the powerful combination of Python and JavaScript. With this hands-on guide, author Kyran Dale teaches you how build a basic dataviz toolchain with best-of-breed Python and JavaScript libraries—including Scrapy, Matplotlib, Pandas, Flask, and D3—for crafting engaging, browser-based visualizations...

    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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