Data Science Weekly Newsletter - Issue 289

Issue #289

June 6 2019

Editor Picks
 
  • This technology can make the Mona Lisa talk (sort of)
    Machine learning researchers have produced a system that can recreate lifelike motion from just a single frame of a person’s face, opening up the possibility of animating not just photos but also paintings. It’s not perfect, but when it works, it is — like much AI work these days — eerie and fascinating...
  • The AI gig economy is coming for you
    The artificial-intelligence industry runs on the invisible labor of humans working in isolated and often terrible conditions—and the model is spreading to more and more businesses...
  • Training a single AI model can emit as much carbon as 5 cars
    In a new paper, researchers at the University of Massachusetts, Amherst, performed a life cycle assessment for training several common large AI models. They found that the process can emit more than 626,000 pounds of carbon dioxide equivalent—nearly five times the lifetime emissions of the average American car (and that includes manufacture of the car itself)...
 
 

A Message from this week's Sponsor:

 

 
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Data Science Articles & Videos

 
  • How To Learn Data Science If You’re Broke
    Over the last year, I taught myself data science. I learned from hundreds of online resources and studied 6–8 hours every day. All while working for minimum wage at a day-care....
  • MelNet: A Generative Model for Audio in the Frequency Domain
    Existing generative models for audio have predominantly aimed to directly model time-domain waveforms. MelNet instead aims to model the frequency content of an audio signal. MelNet can be used to model audio unconditionally, making it capable of tasks such as music generation. It can also be conditioned on text and speaker, making it applicable to tasks such as text-to-speech and voice conversion...
  • Notes on the Limitations of the Empirical Fisher Approximation
    I was debating with myself whether I should write a post about this because it's a superbly written paper that you should probably read in full. There isn't a whole lot of novelty in the paper, but it is a great discussion paper that provides a concise overview of the Fisher information, the empirical Fisher matrix and their connectinos to generalized Gauss-Newton methods...
  • Once again, a neural net tries to name cats
    Last year I trained a neural net to generate new names for kittens, by giving it a list of over 8,000 existing cat names to imitate. Without knowledge of English beyond its list of cat names, it didn’t know what letter combinations to avoid. So I decided to revisit the cat-naming problem, this time using a neural net that had a lot more context...
  • A Quantitative Approach to Product Market Fit
    This article will first go into greater detail of why we believe a quantitative approach to product-market fit is important. Then it will outline in detail three types of analyses we employ at Tribe Capital to understand product-market fit in a given company...
 
 

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...

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

 

Training & Resources

 
  • Deep Learning Models
    A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks...
 
 

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.

     

     
    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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