Roundup 12/12/2016

ICYMI: Top machine learning (ML) and deep learning (DL) stories from last week.

Note to readers: Due to the slower cadence of news as the holidays approach, the daily roundup will be on hiatus until January.  Watch for a roundup of the year in machine learning this week, and a look ahead to 2017. Thank you for reading.


— Microsoft announces Release 9.0 of Microsoft R Server, a bundle of components built on an enhanced R distribution. Highlights of the new release include MicrosoftML, a package of machine learning algorithms; simplified model deployment; support for Spark 2.0; Microsoft R Open (3.3.2) and Microsoft R Client (3.3.2). Serdar Yegualp reports. Linkapalooza here.

— Software AG acquires Zementis for an undisclosed amount. The press release says that Zementis provides software for deep learning. This is incorrect; Zementis offers the ADAPA and UPPI scoring engines, which read PMML documents and produce record-level predictions.

— Uber acquires AI startup Geometric Intelligence for an undisclosed amount.

— NVIDIA offers a deep learning teaching kit for educators, complete with lecture slides, videos, hands-on labs, coding projects, source code solutions, e-books and GPU resources.

— Steve Ranger reports on work by researchers from Cray, Microsoft, and the Swiss National Supercomputing Centre to speed up deep learning on supercomputers. The team has successfully used the Microsoft Cognitive Toolkit (CNTK) to train deep learning on a Cray XC50 (pictured below) with more than 1,000 NVIDIA Tesla P100 GPUs.

Good Reads

— On the Algorithmia blog, Matt Kiser explains why deep learning matters.

— In The Wall Street Journal’s CIO Journal, Sara Castellanos reports on Capital One’s pursuit of explainable machine learning models.

— McKinsey consultants Christoph Glatzel, Matt Hopkins, Tim Lange, and Uwe Weiss explain how retailers use machine learning to drive fresh food stocking.

— In IEEE Spectrum, Deliang Wang explains how his lab at OSU uses deep learning to improve hearing aids.


— Mu Sigma’s Arpit Saxena asks: can weather data improve your predictive models? The answer must be yes, or the article wouldn’t amount to much. Saxena explains some things you should consider when you add weather data to a predictive model.

— On the Lab41 blog, “Patrick C.” argues that sometimes manual feature engineering is easier than feature learning with deep learning.

— In Part One of a series on the MapR blog, Carol McDonald explains how to use k-means in Spark to cluster Uber trips.

— On his personal blog, data scientist Burak Himmetoglu explains how to stack models for better predictions.

— Carlos Perez explains why deep learning is fundamentally different from machine learning. Carlos, co-founder of Intuition Machine, is writing a book called Deep Learning Design Patterns; he blogs regularly here.

Bottom Story of the Week

— The Facebook audience grows older and crankier, and this may harm the social media giant’s revenue.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

This site uses Akismet to reduce spam. Learn how your comment data is processed.