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Applications of R presented at EARL London 2018

转载自:http://blog.revolutionanalytics.com/2018/09/earl-london-2018.html

David Smith


发表于 2018-09-21

During the EARL (Enterprise Applications of the R Language) conference in London last week, the organizers asked me how I thought the conference had changed over the years. (This is the conference’s fifth year, and I’d been to each one.) My response was that it reflected the increasing maturity of R in the enterprise. The early years featured many presentations that were about using R in research and the challenges (both technical and procedural) for integrating that research into the day-to-day processes of the business. This year though, just about every presentation was about R in production, as a mainstream part of the operational infrastructure for analytics. 

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AI-Based Virtual Tutors – The Future of Education?

转载自:https://blogs.technet.microsoft.com/machinelearning/2018/09/21/ai-based-virtual-tutors-the-future-of-education/

ML Blog Team


发表于 2018-09-21

This post is co-authored by Chun Ming Chin, Technical Program Manager, and Max Kaznady, Senior Data Scientist, of Microsoft, with Luyi Huang, Nicholas Kao and James Tayali, students at University of California at Berkeley.

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Distilled News

转载自:https://advanceddataanalytics.net/2018/09/20/distilled-news-865/

Michael Laux


发表于 2018-09-20

Let’s Think in Graphs: Introduction to Graph Theory and its Applications using Python

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Whats new on arXiv

转载自:https://advanceddataanalytics.net/2018/09/20/whats-new-on-arxiv-768/

Michael Laux


发表于 2018-09-20

GritNet 2: Real-Time Student Performance Prediction with Domain Adaptation

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✚ Chart Components and Working On Your Graphics Piece-wise

转载自:https://flowingdata.com/2018/09/20/chart-components/

Nathan Yau


发表于 2018-09-20

Before you can form a set of steps to visualize data, you need to know the components of a chart that you can separate. Like making an outline for an essay, you look for sections that make sense rather than define how many periods and question marks you need to show.

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The Best Programming Languages for Data Science and Machine Learning in 2018

转载自:https://www.codementor.io/dyako/the-best-programming-languages-for-data-science-and-machine-learning-in-2018-nkfl0ukgs

David Yakobovitch


发表于 2018-09-20

In 2018, the field of data science continues to expand at a pace previously unseen in all but the Full Stack Web Development cycle. As a result of faster processing chips and more readable programming languages, including package updates, today data science is becoming available for both consumers and professionals.

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AI, Machine Learning and Data Science Roundup: September 2018

转载自:http://blog.revolutionanalytics.com/2018/09/ai-roundup-september-2018.html

David Smith


发表于 2018-09-20

A monthly roundup of news about Artificial Intelligence, Machine Learning and Data Science. This is an eclectic collection of interesting blog posts, software announcements and data applications from Microsoft and elsewhere that I’ve noted over the past month or so.

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What is P-value?

转载自:https://www.codementor.io/tushar20/what-is-p-value-nkh0pxi5b

Tushar Gupta


发表于 2018-09-20

Every Data Scientist must have come across a question, What is P-value and how do we use it in our statistical analysis?

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Data Notes: How Do Autoencoders Work?

转载自:http://blog.kaggle.com/2018/09/20/data-notes-how-do-autoencoders-work/

Paul Mooney


发表于 2018-09-20

Autoencoders, convolutions, and predicting Google’s stock price: Enjoy these new, intriguing, and overlooked datasets and kernels

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Post-publication peer review: who’s qualified?

转载自:https://andrewgelman.com/2018/09/20/post-publication-peer-review-whos-qualified/

Andrew


发表于 2018-09-20

Gabriel Power writes:

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