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Data Science Weekly Newsletter
Issue
189
July 6, 2017

Editor's Picks



A Message From This Week's Sponsor



STPF is the premier opportunity for outstanding scientists and engineers to learn first-hand about policymaking while contributing their knowledge and analytical skills to address some of today’s most pressing societal challenges. Enhance your career while engaging with policy administrators and thought leaders.
For over 43 years, doctoral level scientists, social scientists, engineers, and health/medical professionals have applied their knowledge and technical expertise to policymaking at the national and international levels. Fellows serve yearlong assignments in all three branches of the federal government and represent a broad range of backgrounds, disciplines and career stages.
For more information, visit: go.stpf-aaas.org/DSW
 


Data Science Articles & Videos

  • Meow Generator
    I experimented with generating faces of cats using Generative adversarial networks (GAN). I wanted to try DCGAN, WGAN and WGAN-GP in low and higher resolutions. I used the CAT dataset (yes this is a real thing!) for my training sample...
  • Using Deep Learning to Reconstruct High-Resolution Audio
    Inspired by the successful applications of deep learning to image super-resolution, there is recent interest in using deep neural networks to accomplish this upsampling on raw audio waveforms. After prototyping several methods, I focused on implementing and customizing recently published research from the 2017 International Conference on Learning Representations...
  • ai.bythebay.io: Chris Moody, AI at Stitch Fix
    I'll review applied deep learning techniques we use at Stitch Fix to understand our client's personal style. Interpretable deep learning models are not only useful to scientists, but lead to better client experiences -- no one wants to interact with a black box virtual assistant. We do this in several ways...
  • Be smarter. Be seetd
    We have built upon our previous seating arrangement efforts and developed a new seating allocation tool - “seetd”. It works by casting the allocation of people to seats (or offices) as an optimization problem, with several different cost-terms which we will discuss below. The ultimate goal is to then find an arrangement which minimises our overall cost function...
  • General Electric Builds an AI Workforce
    As part of its shift toward high-tech businesses, the 125-year-old company is threading artificial intelligence throughout its operations, starting with its scientists....
  • Noisy Networks for Exploration
    We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent's policy can be used to aid efficient exploration...



Jobs

  • Data Scientist - Tala - Santa Monica, CA
    Tala is a mobile technology and data science company that is changing the way credit scoring and financial services work around the world. Tala’s smartphone app instantly evaluates customers for credit using only the data on their devices and delivers customized loans in minutes.
    We are looking for a Data Scientist who can find insights in our unique, diverse, and deep data set. We have just about every sort of data you can imagine -- text, network analysis, image recognition. You’ll be surprised by what connections we’ve found between our different data sources. Our data science team produces and deploys its own models and drives strategic decisions of the entire business team...


Training & Resources

  • Effectively Using Matplotlib
    Now that I have taken the time to learn some of these tools and how to use them with matplotlib, I have started to see matplotlib as an indispensable tool. This post will show how I use matplotlib and provide some recommendations for users getting started or users who have not taken the time to learn matplotlib...
  • Probabilistic programming from scratch
    An introduction to probabilistic programming that neither assumes nor uses statistics, and should be accessible to anyone who can code a little. Bayesian inference without the math or any libraries if you like...


Books



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