docs: Fix markdown link format for StyleTransfer (#344)

Missing closing parentheses for link to StyleTransfer sample causing visual error (link not formatting correctly).

Author: robertyoung2

README.md

@@ -38,7 +38,7 @@ 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.
-- **[StyleTransfer](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/StyleTransfer**: a UWP C# app that uses a custom C++ Video Effect to apply style transfer in real-time to videos. 
+- **[StyleTransfer](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/StyleTransfer)**: a UWP C# app that uses a custom C++ Video Effect to apply style transfer in real-time to videos. 
 - **[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.