Tag Archives: Spark MLLib

Roundup 10/21/2016

Machine learning (ML) and deep learning (DL) content from the past 24 hours. Plus, some AI stuff. Patrick Thibodeau reports that according to Gartner, by 2020 you will say more to a machine than to your spouse. Some jokes write themselves. Methods and Techniques — Tim Spann says that if you can learn to play Atari you can learn TensorFlow. WTF

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Big Analytics Roundup (August 1, 2016)

There are two big stories this week: Apache Spark 2.0 and Apache Mesos 1.0. There’s also a new release from Kylin, and a nice crop of explainers. IEEE Spectrum publishes its third annual ranking of top programming languages, based on twelve metrics drawn from Google Search, Google Trends, Twitter, GitHub, Stack Overflow, Reddit, Hacker News, CareerBuilder, Dice, and the IEEE

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Spark 2.0 Released

The Apache Spark team announces the production release of Spark 2.0.0.  Release notes are here. Read below for details of the new features, together with explanations culled from Spark Summit and elsewhere. Measured by the number of contributors, Apache Spark remains the most active open source project in the Big Data ecosystem. The Spark team guarantees API stability for all production

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Big Analytics Roundup (April 18, 2016)

In hard news this week, Storm hits a milestone with Release 1.0, Google releases TensorFlow 0.8 with distributed computing support, and DataStax announces DataStax Enterprise Graph. And, following on NVIDIA’s DGX-1 announcement last week there are a number of items on Deep Learning featured below. Deep Learning — Adrian Colyer summarizes a paper that summarizes 900 other papers on Deep Learning. —

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Big Analytics Roundup (January 25, 2016)

This week, we have a new release of Spark-TS, Google’s proposal to create an Apache incubator project for Cloud Dataflow, Forrester’s assessment of Hadoop distributions, a couple of funding stories and a nice crop of explainers. Just a reminder that Spark Summit East is coming up February 16-18.  I’ll be delivering a talk in the Executive track on Spark and the

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