Remove 2016 Remove Experimentation Remove Statistics
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. And we can keep repeating this approach, relying on intuition and luck. Why experiment with several parameters concurrently?

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Einstein Studio 1: What it is and what to expect

CIO Business Intelligence

The company has been bundling various forms of automation into its Einstein brand since 2016. For teams that want to boil down their own data into predictive tools, Model Builder will turn all those records of past purchases sitting in the data lake into a big statistical hair ball of tendencies that passes for an AI these days.

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The trinity of errors in applying confidence intervals: An exploration using Statsmodels

O'Reilly on Data

We develop an ordinary least squares (OLS) linear regression model of equity returns using Statsmodels, a Python statistical package, to illustrate these three error types. CI theory was developed around 1937 by Jerzy Neyman, a mathematician and one of the principal architects of modern statistics. and an error term ??

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Belcorp reimagines R&D with AI

CIO Business Intelligence

As Belcorp considered the difficulties it faced, the R&D division noted it could significantly expedite time-to-market and increase productivity in its product development process if it could shorten the timeframes of the experimental and testing phases in the R&D labs.

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Smarter Survey Results and Impact: Abandon the Asker-Puker Model!

Occam's Razor

Hypothesis development and design of experimentation. Econsultancy/Lynchpin provides this description in the report: "There were 960 respondents to our research request, which took the form of a global online survey fielded in May and June 2016. Ok, maybe statistical modeling smells like an analytical skill.

Modeling 128
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Deep Learning Illustrated: Building Natural Language Processing Models

Domino Data Lab

Although it’s not perfect, [Note: These are statistical approximations, of course!] We waved our finger in the air to select 64, so some experimentation and optimization are warranted at your end if you feel like it. representations using RNN encoder-decoder for statistical machine translation. Example 11.6 Joulin, A.,