Here you will learn about transforming, merging, ordering a data frame, changing the column order, removing a variable, sub setting and indexing.
This morning, in our mathematical statistical class, we’ve seen briefly the multinomial distribution, and statistical inference.
We will talk a lot about climate models, and I wanted to play a little bit with those data …
What I love about working at Google is the opportunity to harness cutting-edge machine intelligence for users’ benefit. Two recent Research Blog posts talked about how we’ve used machine learning in the form of deep neural networks to improve voice search and YouTube thumbnails. Today we can share something even wilder — Smart Reply, a deep neural network that writes email.
A quality discussion on the theoretical motivations for deep learning, including distributed representation, deep architecture, and the easily escapable saddle point.
In the python world, there are multiple options for visualizing your data. Because of this variety, it can be really challenging to figure out which one to use when. This article contains a sample of some of the more popular ones and illustrates how to use them to create a simple bar chart.
The R Consortium Infrastructure Steering Committee (chaired by Hadley Wickham) announced today the award of its first grant for an R community development project: $85,000 to Gábor Csárdi to implement the R-Hub project. As a board member of the R Consortium, I’m pleased to say this is a great first project for the R Consortium to get behind, as it aims to ease some of the difficulties associated with developing an R package for submission to CRAN. Currently more than 80% of CRAN submissions are rejected, often due to problems on platforms package developers don’t have access to. When R-hub is ready, package developers will be able to detect and resolve any such issues prior to submitting, making it more likely their package will be accepted while relieving some of the burden on the dedicated volunteers who review CRAN submissions.
Last week I posted a biological example of fitting a non-linear growth curve with Stan/RStan. Today, I want to apply a similar approach to insurance data using ideas by David Clark and James Guszcza .
In this tutorial we’re going to walk you through using the “Text Analysis by AYLIEN” Extension for RapidMiner, to collect and analyze tweets. If you’re new to RapidMiner, or it’s your first time using the Text Analysis Extension you should first read our Getting Started tutorial which takes you through the installation process. Also, If you haven’t got an AYLIEN account, which you’ll need to use the Extension, you can grab one here.