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Can AI Generate Programs to Help Automate Busy Work?

转载自:https://blogs.technet.microsoft.com/machinelearning/2018/09/26/can-ai-generated-code-automate-tedious-elements-of-knowledge-work/

ML Blog Team


发表于 2018-09-26

By Joseph Sirosh, Corporate Vice President and CTO of AI, and Sumit Gulwani, Partner Research Manager, at Microsoft.

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Understanding Regression Error Metrics

转载自:https://www.dataquest.io/blog/understanding-regression-error-metrics/

Christian Pascual


发表于 2018-09-26

Human brains are built to recognize patterns in the world around us. For example, we observe that if we practice our programming everyday, our related skills grow. But how do we precisely describe this relationship to other people? How can we describe how strong this relationship is? Luckily, we can describe relationships between phenomena, such as practice and skill, in terms of formal mathematical estimations called regressions.

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Inside Higher Ed: Pushing the Boundaries of Learning With AI

转载自:https://www.insidehighered.com/digital-learning/article/2018/09/26/academics-push-expand-use-ai-higher-ed-teaching-and-learning

Libby Botsford


发表于 2018-09-26

At Rensselaer Polytechnic Institute, students are immersing themselves in Chinese culture without setting foot outside their classroom.

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Distilled News

转载自:https://advanceddataanalytics.net/2018/09/26/distilled-news-871/

Michael Laux


发表于 2018-09-26

Sequence Modeling with Neural Networks – Part I

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Distilled News

转载自:https://advanceddataanalytics.net/2018/09/26/distilled-news-870/

Michael Laux


发表于 2018-09-26

The Open Data Initiative

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Morph, an open-source tool for data-driven art without code

转载自:https://flowingdata.com/2018/09/26/morph-an-open-source-tool-for-data-driven-art-without-code/

Nathan Yau


发表于 2018-09-26

Morph, by Datavized in collaboration with the Google News Initiative, is a tool to generate abstract images from data:

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You’ve got data on 35 countries, but it’s really just N=3 groups.

转载自:https://andrewgelman.com/2018/09/25/38756/

Andrew


发表于 2018-09-25

Jon Baron points to a recent article, “Societal inequalities amplify gender gaps in math,” by Thomas Breda, Elyès Jouini, and Clotilde Napp (supplementary materials here), and writes:

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Help improve lives through Machine Learning by joining the AWS DeepLens Challenge!

转载自:https://aws.amazon.com/blogs/machine-learning/help-improve-lives-through-machine-learning-by-joining-the-aws-deeplens-challenge/

Sally Revell


发表于 2018-09-25

Today, we’re unveiling a fresh approach to the AWS DeepLens Challenge. We are bringing you four challenges to choose from–sustainability, games, health and inclusivity. Now you can be inspired to create machine learning projects with AWS DeepLens and make a difference at the same time! Use these challenges to gain machine learning experience with fun, collaborative, and inspiring projects. In addition, you’ll be making a positive impact on improving people’s lives and supporting non-profit organizations that benefit our society.

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A Better Example of the Confused By The Environment Issue

转载自:http://www.win-vector.com/blog/2018/09/a-better-example-of-the-confused-by-the-environment-issue/

John Mount


发表于 2018-09-25

Our interference from then environment issue was a bit subtle. But there are variations that can be a bit more insidious.

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Whats new on arXiv

转载自:https://advanceddataanalytics.net/2018/09/25/whats-new-on-arxiv-772/

Michael Laux


发表于 2018-09-25

A Fairness-aware Hybrid Recommender System

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