Tag Archives: Altiscale

The Year in Machine Learning (Part Two)

This is the second installment in a four-part review of 2016 in machine learning and deep learning. Part One, here, covered general trends. In Part Two, we review the year in open source machine learning and deep learning projects. Parts Three and Four will cover commercial machine learning and deep learning software and services. There are thousands of open source projects

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Big Analytics Roundup (September 6, 2016)

Jim Kyung-Soo Liew and Tamas Budavari of Johns Hopkins ask whether Tweet sentiments still predict the stock market. Short Version: they do, but the market has arbitraged away any advantage from trading on the information. So there you have it: the stock market is efficient with respect to fundamental information, technical information, and Tweets. Enterra’s Stephen DeAngelis celebrates the “Algorithmic

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Big Analytics Roundup (August 29, 2016)

TechCrunch reports results of a new study, which says that you really don’t need a co-founder after all. Next, they’ll be telling us we don’t need to floss. Python and R Matt Asay argues that Python is a gateway language that leads data scientists to R. (h/t Oliver Vagner). That’s oversimplified and mostly incorrect. The breadth of R’s analytics functionality tends

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Big Analytics Roundup (May 2, 2016)

Movidius ups the ante for trade show trinkets by releasing what journos describe as supercomputing, neural computing power, vision processing, deep learning, and artificial intelligence on a USB drive.  Roundup here. Last November, IBM’s Paul Zikopoulos snarked at Cloudera for not supporting SparkR. Cloudera’s Sean Owen, responding to a query in the Cloudera Community, notes that SparkR “does not work with other resource managers,” and

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