Data Science Weekly Newsletter - Issue 195

Issue #195

Aug 17 2017

Editor Picks
 
  • Hype or Not? Some Perspective on OpenAI’s DotA 2 Bot
    The OpenAI news came as such a shock. How can this be true? Have there been recent breakthroughs that I wasn’t aware of? As I started looking more into what exactly the DotA 2 bot was doing, how it was trained, and what game environment it was in, I came to the conclusion that it’s an impressive achievement, but not the AI breakthrough the press would like you to believe it is. That’s what this post is about. I would like to offer a sober explanation of what’s actually new...
  • Amazing graphics from the 1950s New York Times archive
    The “morgue” is a smelly storage room in a dark basement just down the street from The New York Times headquarters. About seven million photographs and tens of millions of clippings are stored there. A journalist’s dream, a minimalist’s nightmare...
 
 

A Message from this week's Sponsor:

 

   
 

Data Science Articles & Videos

 
  • Meet the Bregman Divergences
    What I hope to do in this post is gently introduce you to the Bregman divergences, point out some of their interesting properties, and highlight one result that I found surprising and I believe is underappreciated...
  • Captioning Novel Objects in Images
    The task of visual description aims to develop visual systems that generate contextual descriptions about objects in images. Visual description is challenging because it requires recognizing not only objects (bear), but other visual elements, such as actions (standing) and attributes (brown), and constructing a fluent sentence describing how objects, actions, and attributes are related in an image (such as the brown bear is standing on a rock in the forest)...
  • Simple Square Packing Algorithm
    In a recent project the design asked for a component which shows a small number of values in squares. It was important to represent the relation between the values, so they should be mapped to the area and not the size of the squares...
  • Autoregressive Convolutional Neural Networks for Asynchronous Time Series
    We propose 'Significance-Offset Convolutional Neural Network', a deep convolutional network architecture for multivariate time series regression. The model is inspired by standard autoregressive (AR) models and gating mechanisms used in recurrent neural networks. It involves an AR-like weighting system, where the final predictor is obtained as a weighted sum of sub-predictors while the weights are data-dependent functions learnt through a convolutional network...
 
 

Jobs

 
  • Data Scientist - Qubit - London, UK

    We’re looking for a Data Scientist to join our Research team, to help us develop intelligent products around this data, and conduct cutting-edge research into consumer behaviour on the web.

    This is a great opportunity to conduct real R&D around human behaviour. Our data collection tools store more than 1 billion data points every day. Overall, Qubit technology tracks consumer journeys leading to billions of pounds of online spending worldwide every year, for some of the largest names in online retail.

    We’re looking for someone smart and motivated, with experience solving real data analysis problems with statistical and machine learning techniques. As part of our research team you’ll help to understand our ever growing dataset, working closely with other parts of the business to ensure our products are ahead of the competition...
 
 

Training & Resources

 
  • Pandas tips and tricks
    This post includes some useful tips for how to use Pandas for efficiently preprocessing and feature engineering from large datasets...
  • Python Data Science Handbook
    This website contains the full text of the Python Data Science Handbook by Jake VanderPlas; the content is available on GitHub in the form of Jupyter notebooks...
 
 

Books

 

  • The Book of R: A First Course in Programming and Statistics

    "The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis"...


    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 break into Data Science? We've put together a comprehensive guide to get you started. Check it out here! :) - All the best, Hannah & Sebastian
 
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