Monthly Archives: June 2017

R Interface to Spark

June 8, 2017
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R Interface to Spark

SparkR sparklyr

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Data Science for Business – Time Series Forecasting Part 2: Forecasting with timekit

June 8, 2017
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Data Science for Business – Time Series Forecasting Part 2: Forecasting with timekit

In my last post, I prepared and visually explored time series data. Now, I will use this data to test the timekit package for time series forecasting with machine learning. Forecasting In time series forecasting, we use models to predict future ti...

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Run massive parallel R jobs cheaply with updated doAzureParallel package

June 8, 2017
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Run massive parallel R jobs cheaply with updated doAzureParallel package

At the EARL conference in San Francisco this week, JS Tan from Microsoft gave an update (PDF slides here) on the doAzureParallel package . As we've noted here before, this package allows you to easily distribute parallel R computations to an Azure cluster. The package was recently updated to support using automatically-scaling Azure Batch clusters with low-priority nodes, which...

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Introduction to Set Theory and Sets with R

June 8, 2017
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Sets define a ‘collection’ of objects, or things typically referred to as ‘elements’ or ‘members.’ The concept of sets arises naturally when dealing with any collection of objects, whether it be a group of numbers or anything else. Conceptually, the following examples can be defined as a ‘set’: {1, 2,... The post Introduction to Set Theory and Sets...

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Campaign Response Testing no longer published on Udemy

June 8, 2017
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Our free video course Campaign Response Testing is no longer published on Udemy. It remains available for free on YouTube with all source code available from GitHub. I’ll try to correct bad links as I find them. Please read on for the reasons. Udemy recently unilaterally instituted a new policy on free courses: “When a … Continue...

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Neural networks Exercises (Part-1)

June 8, 2017
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Neural networks Exercises (Part-1)

Neural network have become a corner stone of machine learning in the last decade. Created in the late 1940s with the intention to create computer programs who mimics the way neurons process information, those kinds of algorithm have long been believe to be only an academic curiosity, deprived of practical use since they require a Related exercise sets:

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Introducing the MonteCarlo Package

June 8, 2017
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Introducing the MonteCarlo Package

My first R package has been released on CRAN recently. It is named MonteCarlo and aims to make simulation studies as easy as possible – including parallelization and the generation of tables. What Are Simulation Studies Good For? Monte Carlo simulations are an essential tool in statistics and related disciplines. They are routinely used to … Continue...

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Words growing or shrinking in Hacker News titles: a tidy analysis

June 8, 2017
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Words growing or shrinking in Hacker News titles: a tidy analysis

In May, some friends and I built Tagger News, a real-time automatic classifier of Hacker News articles based on their text (see here for more about how we built it). This process started me down some interesting paths, particularly analyzing trends in ...

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Test-driving Microsoft Cognitive Toolkit in R using reticulate

June 8, 2017
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Recently new tools for data science pop up constantly, so it is hard to keep up with the changes and choose those that promise to be useful in the long run.Recently two new solutions were announced that look very promising: reticulate package for R and Microsoft Cognitive Toolkit 2.0 (CNTK). Together with Wit Jakuczun...

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Deep Learning with R

June 8, 2017
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For R users, there hasn’t been a production grade solution for deep learning (sorry MXNET). This post introduces the Keras interface for R and how it can be used to perform image classification. The post ends by providing some code snippets that show Keras is intuitive and powerful. Tensorflow Last January, Tensorflow for R was Related Post

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