We are happy to announce that Microsoft and Intel are partnering to bring optimized deep learning frameworks to Azure. These optimizations are available in a new offering on the Azure marketplace called the Intel Optimized Data Science VM for Linux (Ubuntu).
As a modern developer, you may be eager to build your own deep learning models, but aren’t quite sure where to start. If this is you, I recommend you take a look at the deep learning course from fast.ai. This new fast.ai course helps software developers start building their own state-of-the-art deep learning models.
We used reinforcement learning and CNTK to train a neural network to guess hidden words in a game of Hangman. Our trained model has no reliance on a reference dictionary: it takes as input a variable-length, partially-obscured word (consisting of blank spaces and any correctly-guessed letters) and a binary vector indicating which letters have already been guessed. In the git repository associated with this post, we provide sample code for training the neural network and deploying it in an Azure Web App for gameplay.
This blog will demonstrate end-to-end data science workflow for predictive maintenance using PySpark.
We are excited to announce doAzureParallel – a lightweight R package built on top of Azure Batch, that allows you to easily use Azure’s flexible compute resources right from your R session.