clarify mnist detection

Author: linnealovespie

README.md

@@ -38,8 +38,8 @@ These generic examples show how to use various models and input feeds with Windo
 - **[SqueezeNetObjectDetection\UWP\js](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/SqueezeNetObjectDetection/UWP/js)**: a UWP Javascript app that uses the SqueezeNet model to detect the predominant object in an image. 
 - **[SqueezeNetObjectDetection\Desktop\cpp](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/SqueezeNetObjectDetection/Desktop/cpp)**: a classic desktop C++/WinRT app that uses the SqueezeNet model to detect the predominant object in an image.
 - **[SqueezeNetObjectDetection\NETCore\cs](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/SqueezeNetObjectDetection/Desktop/cpp)**: a .NET Core 2 application that uses the SqueezeNet model to detect the predominant object in an image.
-- **[MNIST\UWP\cs](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/MNIST/Tutorial/cs)**: a UWP C# app that uses the MNIST model to detect numeric characters.
-- **[MNIST\UWP\cppcx](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/MNIST/UWP)**: a UWP C++/CX app that uses the MNIST model to detect numeric characters.
+- **[MNIST\UWP\cs](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/MNIST/Tutorial/cs)**: a UWP C# app that uses the MNIST model to detect handwritten numbers.
+- **[MNIST\UWP\cppcx](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/MNIST/UWP)**: a UWP C++/CX app that uses the MNIST model to detect handwritten numbers.
 - **[CustomTensorization](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/CustomTensorization)**: a Windows Console Application (C++/WinRT) that shows how to do custom tensorization.
 - **[Emoji8](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/Emoji8)**: a UWP C# app that uses the FER Emotion model to evaluate how well the user's facial expressions match randomly selected emojis.
 - **[CustomOperatorCPU](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/CustomOperator)**: a desktop app that defines multiple custom cpu operators. One of these is a debug operator which we invite you to integrate into your own workflow.