Today we’re pleased to announce a new category of community.rstudio.com dedicated to R administrators: https://…/r-admin.
There are already multiple places where you can get help with R, Shiny, the RStudio IDE, and the tidyverse. There are, however, far fewer resources for R admins: people who work with R in production, in large organizations, and in complex environments. We hope this new category will serve as a useful and friendly place to connect with fellow R admins to discuss the issues they deal with. We expect this category to include:
• Discussions about best practices and ideas
• General questions to fellow admins about RStudio Pro products, designed to ease friction in R administrator workflows
• An exchange of ideas on domain-specific use cases and configurations

Stow allows beginners with minimal programming experience to use version control. Version control is a framework that saves previous versions of your work as you develop your projects (Like ‘Track changes’ in MS Word). You can then easily look at previous versions, merge changes, and collaborate with others, but without the headache! Stow uses git under the hood. Git is a popular version control system used by projects such as Android and ggplot2. Don’t worry, you don’t need to know any git to use stow!

Operational disruptions, damaged reputations, data theft, financial loss, and regulatory fines – headlines worldwide have covered every possible downfall of a growing number of successful cyber attacks. Thanks to the media, awareness of the importance of personal data and its value is opening up the eyes of executives, as well as their employees and customers. The European Union is hoping that its General Data Protection Regulation (GDPR) will give businesses the practical guidance they need to tighten data security. Although the mandate will not come into effect until May 25, 2018, Benjamin Spies, IT lawyer and partner at SKW Schwarz, stated that the urgency for data protection is so high that businesses need to comply with the mandate sooner than later. “There such a strong focus on the GDPR right now that it’s very likely that private individuals want to make use of their rights before May 25, 2018. Businesses will be expected to have these rules already in place long before the enforcement date,” Spies said in the Webcast “Doing Business in Europe? General Data Protection Regulation (GDPR): What You Need to Know and Do,” hosted by Americas’ SAP Users’ Group (ASUG).

A 1969 McKinsey article claimed that computers were so dumb that they were not capable of making any decisions. In fact they said, it was human intelligence that drives the dumb machine. Alas, this claim has become a bit of a “joke” over the years, as the modern computers are gradually replacing skilled practitioners in fields across many industries such as architecture, medicine, geology, and education. Artificial Intelligence, Machine Learning, Data Science, and Deep Learning are pushing these changes in ways that are only just being understood. In the current scenario, many buzzwords are being employed in the evolving IT industry, especially in the various research areas around and within Data Science. For many years, the world has known about experiments (with varying degrees of success) in Artificial Intelligence (AI), but recently, rapid strides were made in this field of study, leading to allied research areas of Machine Intelligence, Machine Learning, and now, Deep Learning. So how are these specialized sub-domains under AI similar to or different from each other? This article takes a look.

Web Scraping, which is an essential part of Getting Data, used to be a very straightforward process just by locating the html content with xpath or css selector and extracting the data until Web developers started inserting Javascript-rendered content in the web page. Hence a css selector or an xpath pointing to a browser-generated / javascript-rendered front-end content does not contain html, instead a javascript snippet in the code source. This makes any conventional html scrapper incomplete in the stack since a javascript engine is required to run the extracted javascript code (actual scraped content from the web page) and output the desired result.

In the video presentation below, Matthew Zeiler, PhD, Founder and CEO of Clarifai Inc, speaks about large convolutional neural networks. These networks have recently demonstrated impressive object recognition performance making real world applications possible. However, there was no clear understanding of why they perform so well, or how they might be improved. In this talk, Matt covers a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the overall classifier. Used in a diagnostic role, these visualizations allow us to find model architectures that perform exceedingly well.

The first and second part of this analysis gave the idea that I did too much scrapping and processing and that deserves more analysis to use that information well. In this third and final part I’m also taking a lot of ideas from Julia Silge’s blog. In the GitHub repo of this project you shall find not just Rick and Morty processed subs, but also for Archer, Bojack Horseman, Gravity Falls and Stranger Things. Why? In post post I’m gonna compare the different shows.

Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling.

Bagging and Boosting are both ensemble methods in Machine Learning, but what’s the key behind them? Bagging and Boosting are similar in that they are both ensemble techniques, where a set of weak learners are combined to create a strong learner that obtains better performance than a single one.