Accelerating Azure Databricks Runtime for Machine Learning
Data Scientists love working in the Azure Databricks environment when developing their machine learning and artificial intelligence models. By simply installing Intel-optimized versions of scikit-learn and TensorFlow as described in this blog post, they can see potentially large performance gains that will save them time and money.GPU compute within Windows Subsystem for Linux 2 supports AI and ML workloads
Adding GPU compute support to WSL has been our #1 most requested feature since the first release. Over the last few years, the WSL, Virtualization, DirectX, Windows Driver, Windows AI teams, and our silicon partners have been working hard to deliver this capability.Performance of running NNs across Azure GPU Series Data Science Virtual Machines
First published on MSDN on Nov 22, 2017 One of the question I regularly get, from Student and academics is, Which NN runs the best on Azure?Caffe2MXNetGluonCNTKPyTorchTensorflowKeras(CNTK)ChainerKeras(TF)Lasagne(Theano)Keras(Theano)NNs on AzureSo Azure has lots of support for these including prebuilt Azure Batch shipyard container see https://github.Microsoft’s Batch AI Service - Train & test machine learning models, on pools of GPU machines.
First published on MSDN on Nov 07, 2017 Microsoft Batch AIMicrosoft’s Batch AI Service is a new service that helps you train and test machine learning models, including deep learning models, on pools of GPU machines.How to implement the backpropagation using Python and NumPy
First published on MSDN on Jul 04, 2017 I was recently speaking to a University Academic and we got into the discussion of practical assessments for Data Science Students, One of the key principles students learn is how to implement the back-propagation neural network training algorithm.
