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Preview: Anne Z.

Anne Z.



notes on numbers and other randomness



Last Build Date: Mon, 09 Oct 2017 12:10:25 +0000

 



Daily Links 08/31/2017Anne Z.

Thu, 31 Aug 2017 13:25:06 +0000

The current state of applied data science [Ben Lorica / O’Reilly Radar] Key points from the article: Lack of training data remains the primary bottleneck in machine learning projects Think about features, not algorithms Data enrichment can potentially improve your existing models – this is sometimes overlooked, though this is often not considered as glamorous as […](image)


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Daily Links 08/15/2017Anne Z.

Tue, 15 Aug 2017 16:24:52 +0000

Sheryl Sandberg: Develop Your Voice, Not Your Brand [Theodore Kinni / Stanford GSB ] The idea of developing your personal brand is a bad one, according to Sandberg. “People aren’t brands,” she says. “That’s what products need. They need to be packaged cleanly, neatly, concretely. People aren’t like that.” “Who am I?” asks Sandberg. “I am […](image)


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Daily Links 08/11/2017Anne Z.

Fri, 11 Aug 2017 19:00:22 +0000

Machine Learning vs Statistics: The Texas Death Match of Data Science [Tomm Fawcett & Drew Hardin / Silicon Valley Data Science] Since decisions still have to be made, statistics provides a framework for making betterdecisions. To do this, statisticians need to be able to assess the probabilities associated with various outcomes. And to do that, statisticians use […](image)


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Daily Links 04/11/2017Anne Z.

Tue, 11 Apr 2017 11:45:35 +0000

Demystifying data science The key to a successful analytical model is having a robust set of variables against which to test for their predictive capabilities. And the key to having a robust set of variables from which to test is to get the business users engaged early in the process. How machine learning is shaking […](image)


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Daily Links 04/05/2017Anne Z.

Wed, 05 Apr 2017 15:32:51 +0000

New technology pushes machine smarts to the edge “The set of possible smart edge devices that can be used for industrial control is rapidly expanding as ever more compute and sensing capability moves to the edge,” says Greg Olsen, senior vice president, products, at Falkonry. “As long as the device can transform signal observation into […](image)


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Daily Links 04/04/2017Anne Z.

Tue, 04 Apr 2017 14:51:52 +0000

Emotion Detection and Recognition from Text Using Deep Learning The researchers used a data set of short English text messages labeled by Mechanical Turkers with five emotion classes anger, sadness, fear, happiness, and excitement. A multi-layered neural network was trained to classify text messages by emotion. The model was able to classify anger, sadness, and […](image)


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Has the term AI become meaningless? How about MI insteadAnne Z.

Sat, 25 Mar 2017 19:30:02 +0000

Ian Bogost writing for The Atlantic says that in too many cases today “artificial intelligence” is just another name for a fancy computer program. I don’t see it that way. I know from experience that what most data scientists are building is entirely different from what rank-and-file software developers are building. We use different tools […](image)


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The dialectic of analyticsAnne Z.

Mon, 30 May 2016 18:53:38 +0000

From Gartner’s report The Life of a Chief Analytics Officer: Analytics leaders today often serve two masters: “Classic constituents,” with maintenance and development of traditional solutions for business performance measurement, reporting, BI, dashboard enhancements and basic analytics. “Emerging constituents,” with new ideas, prototypes, exploratory programs, and advanced analytics opportunities. I serve these two masters today in […](image)


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Daily Links 01/27/2015Anne Z.

Tue, 27 Jan 2015 16:03:42 +0000

“What then should we teach about hypothesis testing?” – Statistical Modeling, Causal Inference, and Social Science Statistical Modeling, Causal Inference, and Social Science Traditionally we say: If we find statistical significance, we’ve learned something, but if a comparison is not statistically significant, we can’t say much. (We can “reject” but not “accept” a hypothesis.) But […](image)


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Paradigm shift: From BI to MIAnne Z.Screen Shot 2015-01-21 at 11.43.46 AM

Thu, 22 Jan 2015 16:00:12 +0000

I listened to a Gartner webinar Information 2020: Uncertainty Drives Opportunity given by Frank Buytendijk yesterday and it got me thinking about the evolution (/revolution?) from business intelligence (BI) to machine intelligence (MI). I see this happening but not as fast as I’d like, as jaded as I am about BI. Buytendijk gave me some ideas for understanding this […](image)


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