Data Science Weekly Newsletter - Issue 313

Issue #313

Nov 21 2019

Editor Picks
 
  • Increasing transparency with Google Cloud Explainable AI
    We are excited to announce our latest step in improving the interpretability of AI with Google Cloud AI Explanations. Explanations quantifies each data factor’s contribution to the output of a machine learning model. These summaries help enterprises understand why the model made the decisions it did. You can use this information to further improve your models or share useful insights with the model’s consumers....
  • Deep Learning with PyTorch
    To help developers get started with PyTorch, we’re making the 'Deep Learning with PyTorch' book, written by Luca Antiga and Eli Stevens, available for free to the community...
  • Safety Gym
    We’re releasing Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training...
 
 

A Message from this week's Sponsor:

 

 
Data scientists are in demand on Vettery

Vettery is an online hiring marketplace that's changing the way people hire and get hired. Ready for a bold career move? Make a free profile, name your salary, and connect with hiring managers from top employers today.
 

 

Data Science Articles & Videos


  • It's Sony AI vs. Facebook, Google
    Sony Corp. has launched Sony AI, a new organization to pursue advanced R&D in artificial intelligence. With this move, the Japanese consumer electronics giant intends to go head-to-head with Google and Facebook, competing for AI talent and projects, and targeting a much bigger role in an ever-accelerating global AI race...
  • How to recognize AI snake oil
    Much of what’s being sold as “AI” today is snake oil — it does not and cannot work. Why is this happening? How can we recognize flawed AI claims and push back?...
  • Teachable Machine
    Train a computer to recognize your own images, sounds, & poses. A fast, easy way to create machine learning models for your sites, apps, and more...
  • RecSim: A Configurable Simulation Platform for Recommender Systems
    We have developed RecSim(available here), a configurable platform for authoring simulation environments to facilitate the study of RL algorithms in recommender systems (and CIRs in particular). RecSim allows both researchers and practitioners to test the limits of existing RL methods in synthetic recommender settings...
  • Building A Data Science Portfolio Project Bottom Up
    You are going to build a data science portfolio in order to showcase your skills. You will use it in order to attract potential employers, as well as something to speak about during the actual interview. You have a few ideas of projects. The question now becomes, what do you do next... This article, will cover the basics of the bottom up approach - starting with the data...
 
 

Training*

 
Create D3 Data Visualizations As Fast As You Can Sketch

You need to create a D3.js data visualization to communicate your insights. But... #d3BrokeAndMadeArt! This time, your data join appears to have broken and the JavaScript console shows an error you don't recognize. Last time, you got stuck trying to figure out how to make axes that didn't look like 3rd graded made them. It makes you want to strangle D3 with your bare hands. Just how steep does the D3 learning curve need to be?!

What if you could learn and master D3 quickly and deeply?

Great news! - You can ... Check out DashingD3js.com Screencasts today!

*Sponsored post. If you want to be featured here, or as our main sponsor, contact us!

 

 

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

  • When your data doesn’t fit in memory: the basic techniques
    In this article I’ll cover: Why you need RAM at all; The easiest way to process data that doesn’t fit in memory: spending some money; The three basic software techniques for handling too much data: compression, chunking, and indexing...
  • TensorFlow Lite Transformers w/ Android demo
    Convert Transformers models imported from the 🤗 Transformers library and use them on Android. You can also check out our swift-coreml-transformers repo if you're looking for Transformers on iOS...
  • Faster Neural Networks Straight from JPEG
    In this article, we describe an approach presented at NeurIPS 2018 for making CNNs smaller, faster, and more accurate all at the same time by hacking libjpeg and leveraging the internal image representations already used by JPEG, the popular image format...

 

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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