Here we focus on the MXNet training acceleration: GPU (device) utilized training, distribution training by multiple machines, and active learning (online learning).
Here I show you the step-by-step for scaling the deep learning workloads with MXNet and R. This time, we focus on the scoring phase.
In this post, I describe what is the benefits of Microsoft R technologies (Microsoft R Open, R Server, R Client) for the professional developers with a few lines of code.
In this post, I show you a sample of R-based graph using Power BI for beginner’s understanding. Non-scientists, the business users can easily create the rich graph like correlation, clustering, forecasting, etc, and share for other users.
This post describes how to configure your SAML-based application with new Azure Portal, and some sample code with PHP (simpleSAMLphp) and Node.js (express, passport).
In this post, I summarize the value proposition of Azure DocumentDB and show you how it works for understanding.
In this post we use the Azure Application Insights by the custom code and see how it works behind the scenes for your understanding. (In most cases you don’t need to create the programming code by your own, and the SDK does all.)