In this episode, Robert is joined by Caity Buschlen and Aaron Mast, who show us the resources available as part of Visual Studio subscriptions, including developer tools, cloud services and training.

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In my final article for MSDN, I explore the nuts and bolts of face detection.

Detecting Faces It’s important to point out two distinct terms that are often used interchangeably: face detection and face recognition. Face detection, as the name implies, is limited to detecting the presence of faces in an image. Face recognition involves discerning unique facial characteristics (such as location and shape […]

In my latest column in MSDN Magazine, I explore R and what makes it a powerful and elegant language for exploring and manipulating data.

A robust developer community has emerged around R, with the most popular repository for R packages being the Comprehensive R Archive Network (CRAN). CRAN has various packages that cover anything from Bayesian Accrual Prediction to Spectral Processing for High Resolution Flow Infusion Mass Spectrometry. A complete list of R packages available in CRAN is online at Suffice it to say that R and CRAN provide robust tools for any data science or scientific research project.

In case you didn’t know, I write a monthly column for MSDN Magazine on AI called “Artificially Intelligent”

In the last two articles, I covered one of the most exciting topics in AI in these days: reinforcement learning

Here’s a snippet and link to the full articles on MSDN.

Introduction to Reinforcement Learning

In previous articles, I’ve mentioned both supervised learning and unsupervised learning algorithms. Beyond these two methods of machine learning lays another type: Reinforcement Learning (RL). Formally defined, RL is a computational approach to goal-oriented learning through interaction with the environment under ideal learning conditions.

Like other aspects of AI, many of the algorithms and approaches actively used today trace their origins back to the 1980s ( With the advent of inexpensive storage and on-demand compute power, reinforcement learning techniques have re-emerged.

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A Closer Look at Reinforcement Learning

In last month’s column, I explored a few basic concepts of reinforcement learning (RL), trying both a strictly random approach to navigating a simple environment and then implementing a Q-Table to remember both past actions and which actions led to which rewards. In the demo, an agent working randomly was able to reach the goal state approximately 1 percent of the time and roughly half the time when using a Q-Table to remember previous actions. However, this experiment only scratched the surface of the promising and expanding field of RL.

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As many of you know, I have had a regular column in MSDN Magazine on UWP development for the past 18 months. However, today I am excited to announce that the column will now shift focus to Data Science, AI, and Machine Learning.  This is a natural progression, given the direction of and the Data Driven podcast.

I’m even more excited to announce that the new column, called “Artificially Intelligent” is now available as of the October issue, which is online now.

Here’s a sample:

Over the last 10 years, the focus of many developer and IT organizations was the capture and storage of Big Data. During that time, the notion of what a “large” database size was grew in orders of magnitude from terabytes to petabytes. Now, in 2017, the rush is on to find insights, trends and predictions of the future based on the information buried in these large data stores. Combined with recent advancements in AI research, cloud-based analytics tools and ML algorithms, these large data stores can not only be mined, but monetized.