Roundup 11/23/2016

Machine learning (ML) and deep learning (DL) content from the past 24 hours.

Note to readers: owing to the U.S. Thanksgiving holiday, I do not plan to publish a Roundup on Thursday, November 24 or Friday, November 25. 

AWS Chooses MXNet

Six months after releasing DSSTNE, an internally developed deep learning framework, AWS announces plans to standardize on MXNet. In Fortune, Barb Darrow reports. It seems like a smart move. MXNet is much more mature than DSSTNE, with a 10X larger contributor base. So I guess this move consigns DSSTNE to the dustbin of history.

Good Reads

— Still cleaning up last week’s hot mess of announcements. In Wired, Cade Metz profiles Intel’s chip strategy for machine learning and deep learning. In EETimes, Rick Merritt explains how Intel’s Nervana attacks GPUs.

— On his eponymous blog Elad Gil speculates that Facebook sucks at machine learning.

— Adrian Colyer summarizes Microsoft’s recent paper documenting how they built an application that recognizes conversational speech as well as humans do.

Fundamentals

— Sandeep Raut asks and answers: what is deep learning?

— In Lifehacker, of all places, Eric Ravenscraft explains what neural networks, artificial intelligence and machine learning do in your mobile apps.

Research

— Google Brain’s Mike Shuster (and two other Googlers) explain “zero-shot translation,” or the ability to translate between language pairs that the system has not seen before.

Methods and Techniques

— In DZone, Sibanjan Das explains how to use Spark ML pipelines.

— On the Google Cloud Big Data and Machine Learning Blog, Justyn Kestelyn explains how to use real-time visualization and machine learning to understand urban traffic flows. He uses London as an example.

— Jason Brownlee offers a cheat sheet for improving the performance of machine learning techniques.

Software and Services

— On the main Google Blog, Cindy Teruya touts nine ways the Google Cloud Machine Learning can help businesses.

— Tom Radcliffe compares Python and R for machine learning. He leans toward Python.

— For some reason, Susan May revisits the tiresome old Spark versus MapReduce question. She provides a useful summary of why it’s time to stick a fork in MapReduce — it’s done.

Hardware

— A blogger on Seeking Alpha argues that Intel shouldn’t go to war against NVIDIA, and should offer its own GPUs instead.

Applications

— Remember the glowing reports about the Clinton campaign’s competitive advantage in advanced analytics and machine learning? Like this and this and this and this and this? As it turns out, the Trump campaign had an advanced analytics operation, too. In the future, data science won’t bestow a competitive advantage to one side or the other in political campaigns. It will be table stakes.

Companies

— In Forbes, Jon Markman gushes over NVIDIA’s third quarter earnings report. So does everyone else on Wall Street, it seems, judging by this linkapalooza.

Bottom Story of the Day

— IBM Watson expands consulting support to the Internet of Things.

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