5 Things You Need to Know from IIeX Behaviour UK 2019

Editor’s Note: Recently, we published an overview of key points from the IIeX Behavior US, in North America. We’ve also just had an IIeX Behaviour in the UK.  Here, Matthew Hellon and Ellie Jacobs summarize the highlights from the UK conference.
IIeX Behaviour UK 2019 was an interesting snapshot of how market researchers are adopting behavioral science.

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Confessions from a Quant-a-Holic: A Reflection on IIeX Behavior, Boston

Editor’s Note: Recently, IIeX Behavior US was held in Boston. Many new developments were discussed. Here, Jackie Anderson gives us an overview of key themes from the conference.
Some of my favorite memories from grad school come from the hours spent hunkered in our school’s basement data lab, correlation matrices taped around the room, SPSS outputs so long they’d rival any CVS receipt.

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What Does Stochastic Mean in Machine Learning?

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The behavior and performance of many machine learning algorithms are referred to as stochastic.
Stochastic refers to a variable process where the outcome involves some randomness and has some uncertainty. It is a mathematical term and is closely related to “randomness” and “probabilistic” and can be contrasted to the idea of “deterministic.

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A Gentle Introduction to Stochastic in Machine Learning

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The behavior and performance of many machine learning algorithms are referred to as stochastic.
Stochastic refers to a variable process where the outcome involves some randomness and has some uncertainty. It is a mathematical term and is closely related to “randomness” and “probabilistic” and can be contrasted to the idea of “deterministic.

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Technical SEO Clickability Checklist

While click-through rate (CTR) has everything to do with searcher behavior, there are things you can do to improve your clickability on the SERPs. While meta descriptions and page titles with keywords do impact CTR, we’re going to focus on the technical elements because that’s why you’re here.

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will wolf

As an “in” professional discipline, Machine Learning exhibits a curious behavior: though talent is frustratingly scarce, it’s immensely easy for the individual to obtain. Why? It’s all available online, (largely) free for the taking.
I did not get a bachelor’s degree in this field. Self-teaching at my kitchen table in 2014, I landed a job as a software developer.

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Just evaluating a polynomial: how hard could it be?

The previous post looked at an example of strange floating point behavior taking from book End of Error. This post looks at another example.
This example, by Siegfried Rump, asks us to evaluate
333.75 y6 + x2 (11 x2y2 – y6 – 121 y4 – 2) + 5.5 y8 + x/(2y)
at x = 77617 and y = 33096.
Here we evaluate Rump’s example in single, double, and quadruple precision.

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