**A Shiny App to Create Sentimental Tweets Based on Project Gutenberg Books**

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**Kanri Distance Calculator Free License Version with Demo**

Kanri invites you to a demo where you can receive a free version of the Kanri Distance Calculator, analytics software that takes big data and individualizes results down to individual participant.

**Ranking Popular Deep Learning Libraries for Data Science**

We rank 23 open-source deep learning libraries that are useful for Data Science. The ranking is based on equally weighing its three components: Github and Stack Overflow activity, as well as Google search results.

1 tensorflow

2 keras

3 caffe

4 theano

5 pytorch

6 sonnet

7 mxnet

8 torch

9 cntk

10 dlib

11 caffe2

12 chainer

13 paddlepaddle

14 deeplearning4j

15 lasagne

16 bigdl

17 dynet

18 apache singa

29 nvidia digits

20 matconvnet

21 tflearn

22 nervana neon

23 opennn

**Rethinking 3 Laws of Machine Learning**

We rethink Asimov’s 3 law of robotics to help companies moving to unsupervised machine learning and realize 100% automated predictive information governance (PIG).

**Are Pigs Starting to Fly?**

The holy grail for information governance has always been error-free, intelligent, automation to take end users out of the process.

**How companies can navigate the age of machine learning**

To become a “machine learning company,” you need tools and processes to overcome challenges in data, engineering, and models.

**Statistical Machine Learning with Microsoft ML**

MicrosoftML is an R package for machine learning that works in tandem with the RevoScaleR package. (In order to use the MicrosoftML and RevoScaleR libraries, you need an installation of Microsoft Machine Learning Server or Microsoft R Client.) A great way to see what MicrosoftML can do is to take a look at the on-line book Machine Learning with the MicrosoftML Package Package by Ali Zaidi.

**„One function to rule them all“ – visualization of regression models in #rstats w/ #sjPlot**

I’m pleased to announce the latest update from my sjPlot-package on CRAN. Beside some bug fixes and minor new features, the major update is a new function, plot_model(), which is both an enhancement and replacement of sjp.lm(), sjp.glm(), sjp.lmer(), sjp.glmer() and sjp.int(). The latter functions will become deprecated in the next updates and removed somewhen in the future. plot_model() is a „generic“ plot function that accepts many model-objects, like lm, glm, lme, lmerMod etc. It offers various plotting types, like estimates/coefficient plots (aka forest or dot-whisker plots), marginal effect plots and plotting interaction terms, and sort of diagnostic plots. In this blog post, I want to describe how to plot estimates as forest plots.

**Who knew likelihood functions could be so pretty?**

I just released a new iteration of simstudy (version 0.1.6), which fixes a bug or two and adds several spline related routines (available on CRAN). The previous post focused on using spline curves to generate data, so I won’t repeat myself here. And, apropos of nothing really – I thought I’d take the opportunity to do a simple simulation to briefly explore the likelihood function. It turns out if we generate lots of them, it can be pretty, and maybe provide a little insight.

**Visualization of Regression Models Using visreg**

Abstract Regression models allow one to isolate the relationship between the outcome and an ex planatory variable while the other variables are held constant. Here, we introduce an R package, visreg, for the convenient visualization of this relationship via short, simple function calls. In addition to estimates of this relationship, the package also provides pointwise confidence bands and partial residuals to allow assessment of variability as well as outliers and other deviations from modeling assumptions. The package provides several options for visualizing models with interactions, including lattice plots, contour plots, and both static and interactive perspective plots. The implementation of the package is designed to be fully object-oriented and interface seamlessly with R’s rich collection of model classes, allowing a consistent interface for visualizing not only linear models, but generalized linear models, proportional hazards models, generalized additive models, robust regression models, and many more.

**arulesViz: Interactive Visualization of Association Rules with R**

Abstract Association rule mining is a popular data mining method to discover interesting relation ships between variables in large databases. An extensive toolbox is available in the R-extension package arules. However, mining association rules often results in a vast number of found rules, leaving the analyst with the task to go through a large set of rules to identify interesting ones. Sifting manually through extensive sets of rules is time-consuming and strenuous. Visualization and espe cially interactive visualization has a long history of making large amounts of data better accessible. The R-extension package arulesViz provides most popular visualization techniques for association rules. In this paper, we discuss recently added interactive visualizations to explore association rules and demonstrate how easily they can be used in arulesViz via a unified interface. With examples, we help to guide the user in selecting appropriate visualizations and interpreting the results.

**Machine Learning Explained: Kmeans**

Kmeans is one of the most popular and simple algorithm to discover underlying structures in your data. The goal of kmeans is simple, split your data in k different groups represented by their mean. The mean of each group is assumed to be a good summary of each observation of this cluster.

**Integrating Big Data Tools Into Your Workflow**

Technologies such as smart sensors and the Internet of Things (IoT) are enabling vast amounts of detailed data to be collected from scientific instruments, manufacturing systems, connected cars, aircraft and other sources. With the proper tools and techniques, this data can be used to make rapid scientific discoveries and develop and incorporate more intelligence into products, services, and manufacturing processes.

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