Saturday, October 14, 2017

AI: Deep Learning: research & book

Research in Focus: Deep Learning Research and the Future of AI | Microsoft Research | Channel 9

book online: Deep Learning

Deep Learning lectures

book: Deep Learning | The MIT Press
Deep Learning

Azure Messaging decoded

podcast: Many Kinds of Messaging with Clemens Vasters @ .NET Rocks! vNext
"There are so many messaging options in Azure, how do you choose?"

Windows Desktop Themes downloads

Windows link to "desktop themes" download is moved to Microsoft Store
and there is very few packages available there.

The original download site is still available at:
Featured Desktop Themes - Windows

AI ML: Azure Machine Learning Studio

Artificially Intelligent - Exploring Azure Machine Learning Studio @ MSDN Magazine
By Frank La Vigne | October 2017

Generally Accepted Artificial Intelligence Phrases

data forecasting AI: Time-Series Regression Using a C# Neural Network

Test Run - Time-Series Regression Using a C# Neural Network
By James McCaffrey | October 2017 | Get the Code: C# VB

"The goal of a time-series regression problem is to make predictions based on historical time data.... this article... demonstrate how to perform a time-series regression analysis using rolling-window data combined with a neural network. ...The demo program analyzes the number of airline passengers who traveled each month between January 1949 and December 1960."

Time-Series Regression Line Chart

MSDN Magazine October 2017

Code Downloads for October 2017 MSDN Magazine

Time Series Regression using a C# Neural Network | James D. McCaffrey

James D. McCaffrey | Software Research, Development, Testing, and Education

James McCaffrey at Microsoft Research

GitHub - Azure/Time-series-forecasting-using-CNTK: The code to accompany “Time-series-forecasting-using-CNTK” tutorial on Cortana Intelligence Gallery 

Time Series Forecasting | Cortana Intelligence Gallery (using R)

Time Series Forecasting with Azure ML | NaadiSpeaks

Time Series Data - International Institute of Forecasters

Time Series Data Library - Data provider — DataMarket

International airline passengers: monthly totals in thousands. Jan 49 – Dec 60 — Dataset — DataMarket

RPubs - Air Passengers Forecast (with R language)
"The number of international passengers per month on an airline (Pan Am) in the united states were obtained from the Fedral Aviation Administration for the period 1946-1960. The company used the data to predict future demand before ordering new aircraft and training aircrew."