2017

In case you missed it: September 2017 roundup

October 5, 2017 | David Smith

In case you missed them, here are some articles from September of particular interest to R users. The mathpix package converts images of hand-drawn equations to their LaTeX equivalent. R 3.4.2 is released. Applying image featurization to the problem of classifying wood knots in lumber. Microsoft ML Server 9.2, which provides operationalization ... [Read more...]

The [R] Kenntnis-Tage 2017: A special event

October 5, 2017 | eoda GmbH

Days are getting shorter and the leaves begin to change: autumn is here and this means only one more month until the [R] Kenntnis-Tagen 2017 on November 8 and 9. A diverse agenda, cross-industry networking and a clear business context – many aspects make the [R] Kenntnis-Tage a special event and are reasons for ...
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Working with R

October 5, 2017 | R on Locke Data Blog

I’ve been pretty quiet on the blog front recently. That’s because I overhauled my site, migrating it to Hugo (the foundation of blogdown). Just doing one extra thing on top of my usual workload, I also did another thing. I wrote a book too! I’m a big ... [Read more...]

Experiments with by_row()

October 5, 2017 | Jocelyn Ireson-Paine

I’ve been experimenting with by_row() from the R purrrlyr package. It’s a function that maps over the rows of a data frame, which I thought might be handy for processing our economic model’s parameter files. I didn’t find the documentation for by_row() told me ... [Read more...]

Writing academic articles using R Sweave and LaTeX

October 4, 2017 | Peter Prevos

One of my favourite activities in R is using Markdown to create business reports. Most of my work I export to MS Word to communicate analytical results with my colleagues. For my academic work and eBooks, I prefer LaTeX to produce … Continue reading → The post Writing academic articles using R ...
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Writing academic articles using R Markdown and LaTeX

October 4, 2017 | Peter Prevos

One of my favourite activities in R is using Markdown to create business reports. Most of my work I export to MS Word to communicate analytical results with my colleagues. For my academic work and eBooks, I prefer LaTeX to produce … Continue reading → The post Writing academic articles using R ...
[Read more...]

Introducing the Deep Learning Virtual Machine on Azure

October 4, 2017 | David Smith

A new member has just joined the family of Data Science Virtual Machines on Azure: The Deep Learning Virtual Machine. Like other DSVMs in the family, the Deep Learning VM is a pre-configured environment with all the tools you need for data science and AI development pre-installed. The Deep Learning ... [Read more...]

New R Course: Working with Web Data in R!

October 4, 2017 | Gabriel de Selding

Hi there! We just launched Working with Web Data in R by Oliver Keyes and Charlotte Wickham, our latest R course! Most of the useful data in the world, from economic data to news content to geographic information, lives somewhere on the internet - and this course will teach you ... [Read more...]

RProtoBuf 0.4.11

October 3, 2017 | Thinking inside the box

RProtoBuf provides R bindings for the Google Protocol Buffers ("ProtoBuf") data encoding and serialization library used and released by Google, and deployed fairly widely in numerous projects as a language and operating-system agnostic protocol. A new releases RProtoBuf 0.4.11 appeared on CRAN earlier today. Not unlike the other recent releases, it ... [Read more...]

tfruns: Tools for TensorFlow Training Runs

October 3, 2017 | JJ Allaire

The tfruns package provides a suite of tools for tracking, visualizing, and managing TensorFlow training runs and experiments from R. Use the tfruns package to: Track the hyperparameters, metrics, output, and source code of every training run. Compare hyperparmaeters and metrics across runs to find the best performing model. Automatically ...
[Read more...]

tfruns: Tools for TensorFlow Training Runs

October 3, 2017 | JJ Allaire

The tfruns package provides a suite of tools for tracking, visualizing, and managing TensorFlow training runs and experiments from R. Use the tfruns package to: Track the hyperparameters, metrics, output, and source code of every training run. Compare hyperparmaeters and metrics across runs to find the best performing model. Automatically ...
[Read more...]
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