Tag Archives: TensorFlow

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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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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Roundup 10/17/2016

Top machine learning (ML) and deep learning (DL) stories from last week, plus new content from Friday and the weekend. Note to readers: Big Analytics is now ML/DL. ICYMI: Top Stories of Last Week — The White House publishes a report: Preparing for the Future of Artificial Intelligence. In Fortune, Barb Darrow summarizes. In MIT Tech Review, Will Knight explains. — In the Washington

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Machine Learning Roundup 10/14/2016

Machine learning (ML) and deep learning (DL) content from the past 24 hours. Note to readers: Big Analytics will rebrand as ML/DL on Monday, October 17. Fundamentals — Cynthia Harvey explains the difference between AI, ML, and DL. Issues — Arun Krishnan asks: can algorithms reinforce our biases? — In Nature, Kate Crawford and Ryan Calo note rising use of AI, summarize

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Machine Learning Roundup: 10/5/2016

Machine learning (ML) and deep learning (DL) content from the past 24 hours, plus a few older items. Top stories today: open standards for neural networks; continuing coverage of Australia’s investment in deep learning supercomputers; individual sections on Health Tech and Factory Automation, plus some top-rated books (other than mine.) As always, Adrian Colyer provides the top read of the

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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 (June 27, 2016)

We have announcements from BlueData, Databricks, and DataStax this week, plus a nice crop of explainers. Also, a bit of catch-up, something from May that I missed: Bob Hayes publishes an interesting summary of his recent survey of data scientists. Includes an infographic and slides. Thiemo Fetzer asks: did the weather affect the Brexit vote? Spoiler: he says no. Presented without

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Big Analytics Roundup (May 23, 2016)

Google announces that it has designed an application-specific integrated circuit (ASIC) expressly for deep neural nets. Tech press goes bananas. The chips, branded Tensor Processing Units (TPUs) require fewer transistors per operation, so Google can fit more operations per second into the chip. In about a year of operation, Google has achieved an order of magnitude improvement in performance per watt for

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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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