[doc] Rectify fact

Author: Banyc

docs/essentials/introduction.md

@@ -16,6 +16,8 @@ SciSharp's philosophy allows a large number of machine learning code written in
 
 > It is 2x faster and 1/4 memory occupation of training time in eager mode than python binding. (TensorFlow.NET 0.20-preview2)
 
+## Why over TensorFlowSharp?
+
 In comparison to other projects, like for instance [TensorFlowSharp](https://www.nuget.org/packages/TensorFlowSharp/) which only provide Tensorflow's low-level C++ API and can only run models that were built using Python, Tensorflow.NET also implements Tensorflow's high level API where all the magic happens. This computation graph building layer is still under active development. Once it is completely implemented you can build new Machine Learning models in C# or F#. 
 
 | TensorFlow                 | tf native1.14 | tf native 1.15 | tf native 2.3 |

docs/zh-cn/essentials/introduction.md

@@ -16,7 +16,9 @@
 
 > TensorFlow.NET 有 2x 速度和 1/4 空间占用相比较 python 版. (TensorFlow.NET 0.20-preview2)
 
-举个例子， [TensorFlowSharp](https://www.nuget.org/packages/TensorFlowSharp/) 需要在 python 层之上跑 C# 代码，不够高效。但是 [TF.NET](https://github.com/SciSharp/TensorFlow.NET) 就直接跟 Tensorflow 的 C++ API 打交道，够快！
+## 为啥优于 TensorFlowSharp ？
+
+[TensorFlowSharp](https://www.nuget.org/packages/TensorFlowSharp/) 没有完整的上层 API 来训练模型。但是 [TF.NET](https://github.com/SciSharp/TensorFlow.NET) 的开发活动仍然非常活跃，API 十分齐全，目前已被微软作为官方机器学习框架的底层。
 
 | TensorFlow                 | tf native1.14 | tf native 1.15 | tf native 2.3 |
 | -------------------------- | ------------- | -------------- | ------------- |