![]() ![]() ![]() The installr package for Windows and the updateR package for OS X are particularly good. If you want to try out the Data Science Virtual Machine, the blog post linked below provides links to the documentation and several tutorials to get you started, along with information about the Linux edition of the DSVM.Since the first publication of this post, a couple of packages have emerged to automate this process. And if you use a GPU-enabled instance, the Deep Learning Toolkit extension provides GPU-enabled builds of the Cognitive Toolkit, mxNet, and TensorFlow. There are also improved Deep Learning capabilities, with the latest version of the Microsoft Cognitive Toolkit (formerly called CNTK). If you're new to Julia, the blog post Julia – A Fresh Approach to Numerical Computing provides an introduction. This includes the Julia compiler and popular packages, a debugger, and the Juno IDE. The DSVM also supports the Julia language with the inclusion of the JuliaPro distribution. If you choose a GPU-enabled NC classs instance for your DSVM, the MicrosoftML package can make use of the GPUs for even more performance. This includes the latest R 3.2.2 language engine, the RevoScaleR package for big-data support in R, and also the new MicrosoftML package with several new, high-performance machine learning techniques. Microsoft R Server has also been upgraded to version 9.0. And R Tools for Visual Studio has been upgraded to Version 0.5. ![]() RStudio Desktop is now included in the Data Science Virtual Machine image - no need to install it manually. You now have your choice of integrated development environment to use with R. This update upgrades some existing components and adds some new ones as well. ![]() The Windows edition of the Data Science Virtual Machine (DSVM) was recently updated on the Azure Marketplace. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |