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.
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.Setting up more than 18 GPU Instances on Azure using VMs or Containers
First published on MSDN on Mar 14, 2017 I have been getting a number of questions around the availability of Azure N Series GPU at present we have two SKUs NC (GPU Compute}_ and NV (GPU Visualisation) This blog explains the differences between the SKUs and where NC vs NV hardware instances should be used https://blogs.Deep Learning using CNTK, Caffe, Keras +Theano,Torch, Tensorflow on Docker with Microsoft Azure Batch Shipyard
First published on MSDN on Feb 13, 2017 Learn how to start submitting Deep neural Network training jobs using Azure N series GPU running Ubuntu on Dockers in Azure by using Azure Batch to schedule the jobs to your GPU compute clusters.
