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examples/MRS_and_Machine_Learning/README.md

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 ## MRS_and_Machine_Learning
 This collection of examples shows how to use R and MRS to create 
 machine learning models and showcases how to take advantage of the
-functionality of Mircosoft R Server.
+functionality of Mircosoft R Server. In order to run scripts with 
+`MRS` in the title, it will be necessary to first [install MRS]
+(https://www.microsoft.com/en-us/server-cloud/products/r-server/). 
 
 ### Flight_Delays_Prediction_with_R 
 
+* **R_Flight_Delays_with_MRS_Comparison.R**
+This sample shows how to predict flight delays longer than 15 minutes using R, machine learning
+and historical on-time performance and weather data . 
+When paired with `MRS_Flight_Delays_with_R_Comparison.R`, it provides a step-by-step comparison 
+of the functionality of open source R (a.k.a. CRAN R) and Microsoft R Server. 
+
 ### Flight_Delays_Prediction_with_MRS
+
+* **MRS_Flight_Delays_with_R_comparison.R**
+This sample shows how to predict flight delays longer than 15 minutes using R, machine learning
+and historical on-time performance and weather data . 
+When paired with `MRS_Flight_Delays_with_MRS_Comparison.R`, it provides a step-by-step comparison 
+of the functionality of open source R (a.k.a. CRAN R) and Microsoft R Server. 
+
+* **MRS_Flight_Delays.R**
+This sample shows how to build the same model as `MRS_Flight_Delays_with_R_comparison.R`, but 
+uses MRS best practices and syntax, which can differ substantially from those of R.
   
 ### Bike_Rental_Estimation_with_MRS
 
 * **MRS_Bike_Rental_Estimation.R** 
-  A demand prediction model for bike rentals based on a historical data set.
+  This sample creates a demand prediction model for bike rentals based on a historical data set.
   It uses Microsoft R Server.
-### R_MRO_MRS_Comparison  
-  
-### Benchmarks
+
+### R_MRO_MRS_Comparison
+
+* **R_MRO_MRS_Comparison_Part_1_Functions.R**
+* **R_MRO_MRS_Comparison_Part_2_Capacity.R**
+* **R_MRO_MRS_Comparison_Part_3_Speed.R**
+
+These samples show where the commands, syntax, constructs and performance of 
+R, Microsoft R Open and Microsoft R Server are similar, and where they differ.
+ 
+### Comparisons
 
 * **MRO-MKL-benchmarks.R** 
+Microsoft R Open includes the Intel Math Kernel Library (MKL) 
+for fast, parallel linear algebra 
+computations. This script runs performance benchmarks using different 
+numbers of threads. It requires MRS to be installed.
+
 * **rxGlm-benchmark.R**
+This sample demonstrates how to fit a logistic regression using CRAN R,
+and how the rxGlm() function is dramatically faster and more scalable
+NOTE: The CRAN portion of this comparison requires about 7GB of RAM.
+If your machine has less, this script will crash.
   
 ### Machine Learning  
   
+* **Gradient Boosting Machine.R**
+This sample shows how to create, train and evaluate
+a gradient boosting machine model in R.
+
+* **LASSO Model.R**
+This sample shows how to create, train and evaluate
+a LASSO model in R.
+
+* **Linear Regression and Azure Web Service.R**
+This sample shows how to create, train and evaluate
+a linear regression model in R. It also shows how to deploy 
+that model as a web service in Azure Machine Learning.
 
 ### Data_Exploration
   
 * **Using_ggplot2.R**  
+This sample is an extension of the `A_First_Look_at_R/Introduction_to_ggplot2.R` sample.
+It gives a more extensive tour of ggplot2's functionality including 3D plotting.
+
 * **Import_Data_from_URL.R**
+This sample shows how to load a URL-identified data file into R.
+
 * **Import_Data_from_URL_to_xdf**
+This sample shows how to load a URL-identified data file into MRS as an xdf.
+It requires that MRS be installed.