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(This article was first published on <strong><a href="http://blog.revolutionanalytics.com/2017/10/deep-learning-vm.html"> Revolutions</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<p>A new member has just joined the family of <a rel="nofollow" href="https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/" target="_blank">Data Science Virtual Machines on Azure</a>: The <a rel="nofollow" href="https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/deep-learning-dsvm-overview" target="_blank">Deep Learning Virtual Machine</a>. Like other DSVMs in the family, the Deep Learning VM is a pre-configured environment with all the tools you need for data science and AI development pre-installed. The Deep Learning VM is designed specifically for GPU-enabled instances, and comes with a <a rel="nofollow" href="https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-deep-learning-ai-frameworks" target="_blank">complete suite of deep learning frameworks</a> including Tensorflow, PyTorch, MXNet, Caffe2 and CNTK. It also comes witth example scripts and data sets to get you started on deep learning and AI problems, including:</p>
<ul>
<li><a rel="nofollow" href="https://github.com/ilkarman/DeepLearningFrameworks" target="_blank">Jupyter notebooks to compare performance and accuract of deep learning frameworks</a>; </li>
<li>A how-to guide for building an <a rel="nofollow" href="https://github.com/Azure/cortana-intelligence-product-detection-from-images" target="_blank">object recognition system using deep learning</a>;</li>
<li>Data and scripts to build a LSTM-based hierarchical attention network to <a rel="nofollow" href="https://github.com/anargyri/lstm_han" target="_blank">classify Amazon reviews</a>;</li>
<li><a rel="nofollow" href="https://github.com/Azure/MachineLearningSamples-BiomedicalEntityExtraction" target="_blank">Unstructured text analytics with Biomedical entity extraction</a>.</li>
</ul>
<p>The DLVM along with all the DSVMs also provides a complete suite of data science tools including R, Python, Spark, and much more:</p>
<p><a rel="nofollow" href="https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/" target="_blank"><img border="0" alt="DSVM" title="DSVM" src="http://revolution-computing.typepad.com/.a/6a010534b1db25970b01b7c926d776970b-800wi"></a></p>
<p>There have also been some updates and additions to the tools provided in the entire DSVM family, including:</p>
<ul>
<li>The new <a rel="nofollow" href="http://azureml.azureedge.net/content/apphome/index.html" target="_blank">Azure Machine Learning Workbench</a> (currently in preview, and available as a one-click install from the deployed instance);</li>
<li>The <a rel="nofollow" href="https://github.com/Azure/mmlspark" target="_blank">MMLSpark</a> machine learning library, and the ability to run MMLSpark on a local Spark standalone instance;</li>
<li>H2O <a rel="nofollow" href="https://www.h2o.ai/deep-water/" target="_blank">Deep Water</a> and <a rel="nofollow" href="https://www.h2o.ai/sparkling-water/" target="_blank">Sparkling Water</a> (on Ubuntu only);</li>
<li>The <a rel="nofollow" href="https://www.jetbrains.com/pycharm/" target="_blank">PyCharm</a> Python IDE (on Windows only).</li>
</ul>
<p>All Data Science Virtual Machines, including the Deep Learning Virtual Machine, are available as Windows and Ubuntu Linux instances, and are free of any software charges: pay only for the infrastructure charge according to the power and size of the instance you choose. An Azure account is required, but you can get started with <a rel="nofollow" href="https://azure.microsoft.com/free/" target="_blank">$200 in free Azure credits here</a>.</p>
<p>Microsoft Azure: <a rel="nofollow" href="https://azure.microsoft.com/en-us/services/virtual-machines/data-science-virtual-machines/" target="_blank">Data Science Virtual Machines</a></p>
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