Updated a link to Tensor blog post

README.md

@@ -44,7 +44,7 @@ The sample provides you an example of how you can use Machine Learning and AI in
 
 The evaluate method from CNTK returns a list of floats (0 - 9) predicting the confidence for each of these digits. The highest confidence digit is then displayed in the app.
 
-This sample also introduces Tensor<T>, [Tensor<T>](https://blogs.msdn.microsoft.com/dotnet/2017/11/15/introducing-tensor-for-machine-learning-and-ai-libraries) is an exchange type for homogenous multi-dimensional data for 1 to N dimensions. The motivation behind introducing Tensor<T> is to make it easy for Machine Learning library vendors like CNTK, Tensorflow, Caffe, Scikit-Learn to port their libraries over to .NET with minimal dependencies in place.  Tensor<T> is designed to provide the following characteristics.
+This sample also introduces Tensor<T>, [Tensor<T>](https://blogs.msdn.microsoft.com/dotnet/2017/11/15/introducing-tensor-for-multi-dimensional-machine-learning-and-ai-data/) is an exchange type for homogenous multi-dimensional data for 1 to N dimensions. The motivation behind introducing Tensor<T> is to make it easy for Machine Learning library vendors like CNTK, Tensorflow, Caffe, Scikit-Learn to port their libraries over to .NET with minimal dependencies in place.  Tensor<T> is designed to provide the following characteristics.
 
 To learn more about [Tensor<T> follow our blog post](https://blogs.msdn.microsoft.com/dotnet/2017/11/15/introducing-tensor-for-multi-dimensional-machine-learning-and-ai-data/) and [GitHub repo](https://github.com/dotnet/corefxlab/tree/master/src/System.Numerics.Tensors).