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Machine Reading at Scale – Transfer Learning for Large Text Corpuses

转载自:https://blogs.technet.microsoft.com/machinelearning/2018/10/17/machine-reading-at-scale-transfer-learning-for-large-text-corpuses/

ML Blog Team


发表于 2018-10-17

*This post is authored by Anusua Trivedi, Senior Data Scientist at Microsoft. *

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The Definitive Guide to AI’s “Black Box” Problem

转载自:http://feedproxy.google.com/~r/kdnuggets-data-mining-analytics/~3/k8ymxxvapRc/guide-ai-black-box-problem.html

Dan Clark


发表于 2018-10-17

By Basis Technology. Sponsored Post.

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Use R with Excel: Importing and Exporting Data

转载自:http://feedproxy.google.com/~r/RBloggers/~3/zyaGG9yK7-8/

Kristian Larsen


发表于 2018-10-17
  1. Data Management
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If you did not already know

转载自:https://advanceddataanalytics.net/2018/10/17/if-you-did-not-already-know-515/

Michael Laux


发表于 2018-10-17

Speech2Vec In this paper, we propose a novel deep neural network architecture, Speech2Vec, for learning fixed-length vector representations of audio segments excised from a speech corpus, where the vectors contain semantic information pertaining to the underlying spoken words, and are close to other vectors in the embedding space if their corresponding underlying spoken words are semantically similar. The proposed model can be viewed as a speech version of Word2Vec. Its design is based on a RNN Encoder-Decoder framework, and borrows the methodology of skipgrams or continuous bag-of-words for training. Learning word embeddings directly from speech enables Speech2Vec to make use of the semantic information carried by speech that does not exist in plain text. The learned word embeddings are evaluated and analyzed on 13 widely used word similarity benchmarks, and outperform word embeddings learned by Word2Vec from the transcriptions. …

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Announcing RStudio Package Manager

转载自:http://feedproxy.google.com/~r/RBloggers/~3/95eXPZlySrE/

Sean Lopp


发表于 2018-10-17

We’re excited to announce the general availability of our newest RStudio professional product, RStudio Package Manager. RStudio Package Manager helps your team, department, or entire organization centralize and organize R packages.

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5 Alternatives to the Default R Outputs for GLMs and Linear Models

转载自:http://feedproxy.google.com/~r/RBloggers/~3/ZXB_WZ0LrRM/

Tim Bock


发表于 2018-10-17

The standard summary outputs from the glm and lm summary methods are a case in point. If you have been using R for as long as I have (19 or 20 years…) you will no doubt have a certain affection for them, but to a new user they are both ugly and not optimized to aid interpretation.

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Data Notes: The Secret of Academic Success

转载自:http://blog.kaggle.com/2018/10/17/data-notes-the-secret-of-academic-success/

Paul Mooney


发表于 2018-10-17

From t-SNE to Sex and the City: Enjoy these new, intriguing, and overlooked datasets and kernels

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Mindstrong Health: Sr Data Scientist / Machine Learning, Statistics, Coding [Palo Alto, CA]

转载自:http://feedproxy.google.com/~r/kdnuggets-data-mining-analytics/~3/ildpymx3oOI/10-17-mindstrong-health-data-scientist.html

Matt Mayo Editor


发表于 2018-10-17

At: Mindstrong Health Location: Palo Alto, CAWeb: mindstronghealth.comPosition: Sr Data Scientist / Machine Learning, Statistics, Coding

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A small logical change with big impact

转载自:http://feedproxy.google.com/~r/RBloggers/~3/E5X5IrJ1kjs/

David Smith


发表于 2018-10-16

In R, the logical || (OR) and && (AND) operators are unique in that they are designed only to work with scalar arguments. Typically used in statements like

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The AAA tranche of subprime science, revisited

转载自:https://andrewgelman.com/2018/10/16/aaa-tranche-subprime-science-revisited/

Andrew


发表于 2018-10-16

Tom Daula points us to this article, “Mortgage-Backed Securities and the Financial Crisis of 2008: A Post Mortem,” by Juan Ospina and Harald Uhlig. Not our usual topic at this blog, but then there’s this bit on page 11:

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