In the latest Redmonk language rankings, R has risen to the #12 position, up from #14 in the June 2017 rankings. Python remains steady in the #3 position. The Redmonk rankings are based on activity in StackOverflow (as a proxy for user engagement) and Github (as a proxy for developer engagement).

A tutorial illustrating how to create a deep neural network model that accepts an aerial image as input and returns a land cover label (forested, water, etc.) for every pixel in the image. Microsoft’s Cognitive Toolkit (CNTK) is used to train and evaluate the model on an Azure Geo AI Data Science Virtual Machine or an Azure Batch AI GPU cluster. The method shown was developed in collaboration between the Chesapeake Conservancy, ESRI, and Microsoft Research as part of Microsoft’s AI for Earth initiative.

Demo of running Neural Networks across different frameworks in Python. Link to GitHub.

With Docker containers as the development environment for your deep learning projects, you can hit the ground running. You are spared the overhead of installing and setting up the environment for the various frameworks and can start working on your deep learning projects right away. Scripts are guaranteed to run everywhere and will run the same every time.

This update improves automatic timezone detection on some systems, and adds fixes for a some unusual corner cases in the statistics library. For a complete list of the changes, check the NEWS file for R 3.4.4 or follow the link above.

A roundup of on demand Azure cloud data and AI services training.

“You can’t do data science in a GUI”, by Hadley Wickham, Chief Scientist at RStudio. Talk given at Chicago Chapter of the ACM, 7 March 2018.

Overview of what Windows ML can do for developers; and show how to easily use only three lines of code to develop UWP application that runs AI locally on Windows 10 devices.

Lucas Brodzinksi from the Windows Machine Learning team gives us an overview of what is Windows ML, why it is important, and how to get started.

In this episode, you will get an overview of Microsoft’s AI platform and we will do a walkthrough of different components of Azure ML Services.

In this episode, you will get started with Azure Machine Learning Services by setting up an Experimentation account, a Model Management account and AML Workbench. You will learn about the different features of Azure ML Workbench and its ability to speed up data wrangling.

In this episode of the AI Show, machine learning is gently introduced from the standpoint of the algorithm and model. It starts with the simplest machine learning, linear regression, and ends on a de

This session is a primer to introduce the concepts of deep learning with a specific focus on computer vision. It covers concepts including CNN’s (Convolutional Neural Networks), deep learning and transfer learning. It was created as an introduction for people getting started with machine learning and specifically deep learning to explain some of the commonly used terms and introduce some of the popular approaches to solving computer vision challenges.

A dive into when and why one would use deep learning over classical machine learning. While many tasks can be performed cheaply and well with classical machine learning and packages like scikit-learn, every once in a while, a task is better suited for a neural network architecture implemented with deep learning methods – e.g. large amounts of data or insufficient accuracy with other methods. Watch to find out more and hear about some Python packages to make life easier.

The paper “Building Blocks of Interpretability” is available here.

The paper “DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks” and its demo is available here.

The paper “Avatar Digitization From a Single Image For Real-Time Rendering” is available here.

Project Malmö is a platform for Artificial Intelligence experimentation and research built on top of Minecraft. We aim to inspire a new generation of research into challenging new problems presented by this unique environment.

The best leaders already know how their organisation is performing, and that it has improved during their tenure - and they can prove it because they practise evidence-based leadership. This book offers a clear blueprint for building on your existing skills and performance management systems to build a truly high performance organisation.