Remove 2006 Remove Metrics Remove Modeling Remove Optimization
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Towards optimal experimentation in online systems

The Unofficial Google Data Science Blog

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well.

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Analytics On The Bleeding Edge: Transforming Data's Influence

Occam's Razor

From 2006: Is Real-Time Analytics Really Relevant? ). In our in-flight optimization journey thus far, we have worked to identify signals that are believable, and identifying at which point they become believable (ex: statistically significant). The benchmark for the beautiful metric AVOC is 15.3%. It sounds complex, it is not.

Analytics 131
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The Complete Digital Analytics Ecosystem: How To Win Big

Occam's Razor

Digital Analytics Ecosystem: Optimal Execution: Three Phases. Digital Analytics Ecosystem: Optimal Execution: Timing Expectations. At the core of everything you will do in digital analytics is the concept of metrics. How do you define a metric: It is simply a number. Your digital analytics tools are full of metrics.

Analytics 150
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Data Science, Past & Future

Domino Data Lab

how “the business executives who are seeing the value of data science and being model-informed, they are the ones who are doubling down on their bets now, and they’re investing a lot more money.” and drop your deep learning model resource footprint by 5-6 orders of magnitude and run it on devices that don’t even have batteries.

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Excellent Analytics Tip #27: Chase Smart Calculated Metrics!

Occam's Razor

The specific metric I've been mad about since day one of this blog ( May 14th, 2006! ) Built into that is the mental model that if you visit a website, then every Visit has to result in money for the site owner. Metrics create incentives, bad metrics create bad incentives. is Conversion Rate.

Metrics 88
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Position2’s Arena Calibrate helps customers drive marketing efficiency with Amazon QuickSight Embedded

AWS Big Data

Position 2 was established in 2006 in Silicon Valley and has a clientele spanning American Express, Lenovo, Fujitsu, and Thales. After all that, we were still missing out on proactive analysis that identifies trends and uncovers optimization opportunities. We work with clients ranging from VC-funded startups to Fortune 500 firms.

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Building a Named Entity Recognition model using a BiLSTM-CRF network

Domino Data Lab

In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated corpus and Keras. The model achieves relatively high accuracy and all data and code is freely available in the article. How to build a statistical Named Entity Recognition (NER) model.

Modeling 111