Author Archives: Thomas W. Dinsmore

A Note to Readers

As we welcome the New Year — belatedly — I want to extend many thanks to those who read the scribblings on this blog. I’m particularly grateful for the great response to my recent four-part review of the year in machine learning and deep learning. Looking out over the next several months, some exciting long-form stories are coming: the SQL-in-Hadoop

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The Year in Machine Learning (Part Four)

This is the fourth installment in a four-part review of 2016 in machine learning and deep learning. — Part One covered Top Trends in the field, including concerns about bias, interpretability, deep learning’s explosive growth, the democratization of supercomputing, and the emergence of cloud machine learning platforms. — Part Two surveyed significant developments in Open Source machine learning projects, such as R, Python, Spark, Flink,

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The Year in Machine Learning (Part Three)

This is the third installment in a four-part review of 2016 in machine learning and deep learning. In Part One, I covered Top Trends in the field, including concerns about bias, interpretability, deep learning’s explosive growth, the democratization of supercomputing, and the emergence of cloud machine learning platforms. In Part Two, I surveyed significant developments in Open Source machine learning projects, such as R, Python,

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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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The Year in Machine Learning (Part One)

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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Bottom ML/DL Story of 2016

  Many thanks to readers who voted in the “Bottom ML/DL Story of 2016” poll. The choices were: Amazon Web Services releases DSSTNE to open source Dato rebrands as Turi IBM rebrands an existing Spark service as The Data Science experience IBM rebrands an existing SPSS service as Watson Machine Learning IBM standardizes graph analytics on Apache Tinkerpop Movidius launches

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