Tag Archives: Cray Urika-GX

The Year in Machine Learning (Part One)

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This is the first installment in a four-part review of 2016 in machine learning and deep learning. In the first post, we look back at ML/DL news organized in five high-level topic areas: Concerns about bias Interpretable models Deep learning accelerates Supercomputing goes mainstream Cloud platforms build ML/DL stacks In Part Two, we cover developments in each of the leading open

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Big Analytics Roundup (May 31, 2016)

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Google’s TPU announcement on May 18 continues to reverberate in the tech press. In Forbes, HPC expert Karl Freund dissects Google’s announcement, suggesting that Google is indulging in a bit of hocus-pocus to promote its managed services.  Freund believes that TPUs are actually used for inference and not for model training; in other words, they replace CPUs rather than GPUs. Read

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