Monthly Archives: July 2017

What analysis programs drive conservation science?

July 31, 2017
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What analysis programs drive conservation science?

What analysis programs drive conservation science? With the International Congress for Conservation Biology on at the end of July I was wondering, what analysis programs are supporting conservation science? And, what programs support spatial analysis ...

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How to use H2O with R on HDInsight

July 31, 2017
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H2O.ai is an open-source AI platform that provides a number of machine-learning algorithms that run on the Spark distributed computing framework. Azure HDInsight is Microsoft's fully-managed Apache Hadoop platform in the cloud, which makes it easy to spin up and manage Azure clusters of any size. It's also easy to to run H2O on HDInsight: H2O AI Platform is...

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Counterfactual estimation on nonstationary data, be careful!!!

July 31, 2017
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Counterfactual estimation on nonstationary data, be careful!!!

By Gabriel Vasconcelos In a recent paper that can be downloaded here, Carvalho, Masini and Medeiros show that estimating counterfactuals in a non-stationary framework (when I say non-stationary it means integrated) is a tricky task. It is intuitive that the … Continue reading →

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15 Jobs for R users (2017-07-31) – from all over the world

July 31, 2017
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15 Jobs for R users (2017-07-31) – from all over the world

To post your R job on the next post Just visit this link and post a new R job to the R community. You can post a job for free (and there are also “featured job” options available for extra exposure). Current R jobs Job seekers: please follow the links below to learn more and apply for your R job of interest: Featured Jobs Freelance Data Scientists...

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Machine Learning Explained: Dimensionality Reduction

July 31, 2017
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Machine Learning Explained: Dimensionality Reduction

Dealing with a lot of dimensions can be painful for machine learning algorithms. High dimensionality will increase the computational complexity, increase the risk of overfitting (as your algorithm has more degrees of freedom) and the sparsity of the data will grow. Hence, dimensionality reduction will project the data in a space with less dimension to The post Machine Learning...

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Google Vision API in R – RoogleVision

July 31, 2017
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Google Vision API in R – RoogleVision

Using the Google Vision API in R Utilizing RoogleVision After doing my post last month on OpenCV and face detection, I started looking into other algorithms used for pattern detection in images. As it turns out, Google has done a phenomenal job with their Vision API. It’s absolutely incredible the amount of information it can

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Upcoming Talk at the Bay Area R Users Group (BARUG)

July 31, 2017
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Upcoming Talk at the Bay Area R Users Group (BARUG)

Next Tuesday (August 8) I will be giving a talk at the Bay Area R Users Group (BARUG). The talk is titled Beyond Popularity: Monetizing R... The post Upcoming Talk at the Bay Area R Users Group (BARUG) appeared first on AriLamstein.com.

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sparklyr 0.6

July 30, 2017
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We’re excited to announce a new release of the sparklyr package, available in CRAN today! sparklyr 0.6 introduces new features to: Distribute R computations using spark_apply() to execute arbitrary R code across your Spark cluster. You can now use all of your favorite R packages and functions in a distributed context. Connect to External Data Sources using spark_read_source(), spark_write_source(), spark_read_jdbc() and...

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Data visualization with googleVis exercises part 9

July 30, 2017
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Data visualization with googleVis exercises part 9

Histogram & Calendar chart This is part 9 of our series and we are going to explore the features of two interesting types of charts that googleVis provides like histogram and calendar charts. Read the examples below to understand the logic of what we are going to do and then test yous skills with the Related exercise sets: Data Visualization...

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Matching, Optimal Transport and Statistical Tests

July 30, 2017
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Matching, Optimal Transport and Statistical Tests

To explain the “optimal transport” problem, we usually start with Gaspard Monge’s “Mémoire sur la théorie des déblais et des remblais“, where the the problem of transporting a given distribution of matter (a pile of sand for instance) into another (an excavation for instance). This problem is usually formulated using distributions, and we seek the “optimal” transport from one...

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