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

There was something about them that made me uneasy, some longing and at the same time some deadly fear – Dracula (Stoker, Bram) Twitter is a very good source of inspiration. Some days ago I came across with this: The tweet refers to a presentation (in Spanish) available here, which is a very concise and well illustrated document about the state-of-the-art of text mining in R. I discovered there several libraries that I will try to use in the future. In this experiment I have used one of them: the syuzhet package. As can be read in the documentation: this package extracts sentiment and sentiment-derived plot arcs from text using three sentiment dictionaries conveniently packaged for consumption by R users. Implemented dictionaries include syuzhet (default) developed in the Nebraska Literary Lab, afinn developed by Finn Arup Nielsen, bing developed by Minqing Hu and Bing…
Original Post: A Shiny App to Create Sentimental Tweets Based on Project Gutenberg Books

Loading R Packages: library() or require()?

When I was an R newbie, I was taught to load packages by using the command library(package). In my Linear Models class, the instructor likes to use require(package). This made me wonder, are the commands interchangeable? What’s the difference, and which command should I use? Interchangeable commands . . . The way most users will use these commands, most of the time, they are actually interchangeable. That is, if you are loading a library that has already been installed, and you are using the command outside of a function definition, then it makes no difference if you use “require” or “library.” They do the same thing. … Well, almost interchangeable There are, though, a couple of important differences. The first one, and the most obvious, is what happens if you try to load a package that has not previously been…
Original Post: Loading R Packages: library() or require()?

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. The book includes worked examples on several topics: Exploratory data analysis and feature engineering Regression models Classification models for computer vision Convolutional neural networks for computer vision Natural language processing Transfer learning with pre-trained DNNs The book is part of Ali’s in-person workshop “Statistical Machine Learning with MicrosoftML”, and you can find further materials including data and scripts at this Github repository. If you’d like to experience the workshop in person, Ali will be presenting it…
Original Post: Statistical Machine Learning with Microsoft ML

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. The book includes worked examples on several topics: Exploratory data analysis and feature engineering Regression models Classification models for computer vision Convolutional neural networks for computer vision Natural language processing Transfer learning with pre-trained DNNs The book is part of Ali’s in-person workshop “Statistical Machine Learning with MicrosoftML”, and you can find further materials including data and scripts at this Github repository. If you’d like to experience the workshop in person, Ali will be presenting it…
Original Post: Statistical Machine Learning with Microsoft ML

Your Complete Guide to Predictive Analytics World – Oct 29-Nov 2 in New York City

[unable to retrieve full-text content]Predictive Analytics World for Business is slated for Oct 29-Nov 2 in New York City. See for yourself precisely how Fortune 500 analytics competitors and other top practitioners deploy predictive modeling and machine learning, and the kind of business results they achieve.
Original Post: Your Complete Guide to Predictive Analytics World – Oct 29-Nov 2 in New York City

Announcing OpenFermion: The Open Source Chemistry Package for Quantum Computers

Posted by Ryan Babbush and Jarrod McClean, Quantum Software Engineers, Quantum AI Team“The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known, and the difficulty is only that the exact application of these laws leads to equations much too complicated to be soluble.”-Paul Dirac, Quantum Mechanics of Many-Electron Systems (1929)In this passage, physicist Paul Dirac laments that while quantum mechanics accurately models all of chemistry, exactly simulating the associated equations appears intractably complicated. Not until 1982 would Richard Feynman suggest that instead of surrendering to the complexity of quantum mechanics, we might harness it as a computational resource. Hence, the original motivation for quantum computing: by operating a computer according to the laws of quantum mechanics, one could efficiently unravel exact simulations of nature. Such simulations…
Original Post: Announcing OpenFermion: The Open Source Chemistry Package for Quantum Computers

Ranking Popular Deep Learning Libraries for Data Science

[unable to retrieve full-text content]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.
Original Post: Ranking Popular Deep Learning Libraries for Data Science

New Poll: When will demand for Data Scientists/Machine Learning experts begin to decline?

[unable to retrieve full-text content]New KDnuggets Poll examines how long the current high demand for Data Scientists/Machine Learning experts will last. Please vote and we will analyze and report the results.
Original Post: New Poll: When will demand for Data Scientists/Machine Learning experts begin to decline?

The Return of Free Data and Possible Volatility Trading Subscription

This post will be about pulling free data from AlphaVantage, and gauging interest for a volatility trading subscription service. So first off, ever since the yahoos at Yahoo decided to turn off their free data, the world of free daily data has been in somewhat of a dark age. Well, thanks to http://blog.fosstrading.com/2017/10/getsymbols-and-alpha-vantage.html#gpluscommentsJosh Ulrich, Paul Teetor, and other R/Finance individuals, the latest edition of quantmod (which can be installed from CRAN) now contains a way to get free financial data from AlphaVantage since the year 2000, which is usually enough for most backtests, as that date predates the inception of most ETFs. Here’s how to do it. First off, you need to go to alphaVantage, register, and https://www.alphavantage.co/support/#api-keyget an API key. Once you do that, downloading data is simple, if not slightly slow. Here’s how to do it. require(quantmod) getSymbols(‘SPY’,…
Original Post: The Return of Free Data and Possible Volatility Trading Subscription