Roundup 12/9/2016

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

Microsoft Releases R Server 9.0

On the Cortana Intelligence and Machine Learning Blog, 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 through mrsdeploy, a package that converts R models to web services, and Swagger, a bundled open source language-agnostic interface to REST APIs

— Support for Spark 2.0 through ScaleR, a distributed machine learning package. ScaleR can now read Hive and Parquet data sources into Spark DataFrames

— The latest releases of Microsoft R Open (3.3.2) and Microsoft R Client (3.3.2)

The MicrosoftML package includes data transformation functions and machine learning algorithms developed internally at Microsoft. The transform functions enable the user to concatenate columns, hash categorical variables, convert categoricals to an indicator array, select features and featurize text. Algorithms include a fast linear model, logistic regression, single-class SVM, fast decision tree, fast random forest and neural networks.

Serdar Yegualp reports. Linkapalooza here.

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Issues

— William Vorhies warns data scientists about government regulations that will soon impact the field. Surprisingly, he does not mention the EU’s General Data Protection Regulation, set to go into effect May 25, 2018. I’m currently writing a piece on GDPR, which I expect to publish soon.

— In HBR, the Ivy Leaguers who produced the Vietnam War and Enron deliver a guide to solving social problems with machine learning. It’s actually not a bad piece, though it reads as if they took HBR’s recently published guide to solving business problems with machine learning, crossed out “business” and replaced it with “social.” Can we all agree that there is something called methodology?

Fundamentals

— In Forbes, Bernard Marr takes another whack at defining the differences between machine learning, deep learning, and artificial intelligence.

— In a publication that calls itself University Herald, Chris Brandt discovers that machine learning is a thing and artificial intelligence is a thing and they are two different things.

Methods and Techniques

— In a Databricks Webinar, Joseph K. Bradley and Jules S. Damji explain how to migrate workloads from Spark’s RDD-based machine learning API to the new DataFrames-based API. There are notebooks with working examples.

Software/Services

— RStudio’s Joseph Rickert lists his favorite new packages in R among the 189 added in November, including 9 packages for machine learning.

Hardware

— In an Inside HPC podcast, NVIDIA’s Bryan Catanzaro predicts where deep learning is going next.

Bottom Story of the Day

— Audi trains a toy car to park itself. Linkapalooza here. Now, if they can just bring a car to market without cheating on the emissions rules.

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