Tag Archives: Dataiku

More Notes on SAS

Last week’s post on SAS provoked numerous comments on this blog, and over on Hacker News. Here are some excerpts. I’ve edited for length and grammar. Feel free to read the originals. SAS employee Scott Mongeau writes: Journeying through the vast graveyard of open source vanity projects I come in on as a mop-up-agent on in any given month, one

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Notes on the Forrester “Wave”

Forrester recently published the Wave™ for Predictive Analytics And Machine Learning Solutions. You can purchase a copy from Forrester for $2,495, or get a free copy here. When Forrester last delivered this analysis in 2015, they called it the Wave™ for Big Data Predictive Analytics Solutions. So, I guess that Big Data is “out” and machine learning is “in.” The chart below shows the

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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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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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