Fix broken link in home page (#3109)

* fix broken link in home page

* fix broken link in zh home page

Co-authored-by: kvartet <kvartet>

README.md

@@ -14,7 +14,7 @@
 
 [简体中文](README_zh_CN.md)
 
-**NNI (Neural Network Intelligence)** is a lightweight but powerful toolkit to help users **automate** <a href="docs/en_US/FeatureEngineering/Overview.md">Feature Engineering</a>, <a href="docs/en_US/NAS/Overview.md">Neural Architecture Search</a>, <a href="docs/en_US/Tuner/BuiltinTuner.md">Hyperparameter Tuning</a> and <a href="docs/en_US/Compressor/Overview.md">Model Compression</a>.
+**NNI (Neural Network Intelligence)** is a lightweight but powerful toolkit to help users **automate** <a href="docs/en_US/FeatureEngineering/Overview.md">Feature Engineering</a>, <a href="docs/en_US/NAS/Overview.md">Neural Architecture Search</a>, <a href="docs/en_US/Tuner/BuiltinTuner.md">Hyperparameter Tuning</a> and <a href="docs/en_US/Compression/Overview.md">Model Compression</a>.
 
 The tool manages automated machine learning (AutoML) experiments, **dispatches and runs** experiments' trial jobs generated by tuning algorithms to search the best neural architecture and/or hyper-parameters in **different training environments** like <a href="docs/en_US/TrainingService/LocalMode.md">Local Machine</a>, <a href="docs/en_US/TrainingService/RemoteMachineMode.md">Remote Servers</a>, <a href="docs/en_US/TrainingService/PaiMode.md">OpenPAI</a>, <a href="docs/en_US/TrainingService/KubeflowMode.md">Kubeflow</a>, <a href="docs/en_US/TrainingService/FrameworkControllerMode.md">FrameworkController on K8S (AKS etc.)</a>, <a href="docs/en_US/TrainingService/DLTSMode.md">DLWorkspace (aka. DLTS)</a>, <a href="docs/en_US/TrainingService/AMLMode.md">AML (Azure Machine Learning)</a> and other cloud options.
 
@@ -137,22 +137,22 @@ Within the following table, we summarized the current NNI capabilities, we are g
               <li><a href="docs/en_US/NAS/TextNAS.md">TextNAS</a></li>
             </ul>
           </ul>
-          <a href="docs/en_US/Compressor/Overview.md">Model Compression</a>
+          <a href="docs/en_US/Compression/Overview.md">Model Compression</a>
           <ul>
             <b>Pruning</b>
             <ul>
-              <li><a href="docs/en_US/Compressor/Pruner.md#agp-pruner">AGP Pruner</a></li>
-              <li><a href="docs/en_US/Compressor/Pruner.md#slim-pruner">Slim Pruner</a></li>
-              <li><a href="docs/en_US/Compressor/Pruner.md#fpgm-pruner">FPGM Pruner</a></li>
-              <li><a href="docs/en_US/Compressor/Pruner.md#netadapt-pruner">NetAdapt Pruner</a></li>
-              <li><a href="docs/en_US/Compressor/Pruner.md#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
-              <li><a href="docs/en_US/Compressor/Pruner.md#admm-pruner">ADMM Pruner</a></li>
-              <li><a href="docs/en_US/Compressor/Pruner.md#autocompress-pruner">AutoCompress Pruner</a></li>
+              <li><a href="docs/en_US/Compression/Pruner.md#agp-pruner">AGP Pruner</a></li>
+              <li><a href="docs/en_US/Compression/Pruner.md#slim-pruner">Slim Pruner</a></li>
+              <li><a href="docs/en_US/Compression/Pruner.md#fpgm-pruner">FPGM Pruner</a></li>
+              <li><a href="docs/en_US/Compression/Pruner.md#netadapt-pruner">NetAdapt Pruner</a></li>
+              <li><a href="docs/en_US/Compression/Pruner.md#simulatedannealing-pruner">SimulatedAnnealing Pruner</a></li>
+              <li><a href="docs/en_US/Compression/Pruner.md#admm-pruner">ADMM Pruner</a></li>
+              <li><a href="docs/en_US/Compression/Pruner.md#autocompress-pruner">AutoCompress Pruner</a></li>
             </ul>
             <b>Quantization</b>
             <ul>
-              <li><a href="docs/en_US/Compressor/Quantizer.md#qat-quantizer">QAT Quantizer</a></li>
-              <li><a href="docs/en_US/Compressor/Quantizer.md#dorefa-quantizer">DoReFa Quantizer</a></li>
+              <li><a href="docs/en_US/Compression/Quantizer.md#qat-quantizer">QAT Quantizer</a></li>
+              <li><a href="docs/en_US/Compression/Quantizer.md#dorefa-quantizer">DoReFa Quantizer</a></li>
             </ul>
           </ul>
           <a href="docs/en_US/FeatureEngineering/Overview.md">Feature Engineering (Beta)</a>
@@ -331,8 +331,8 @@ With authors' permission, we listed a set of NNI usage examples and relevant art
    * [Hyperparameter Tuning for Matrix Factorization](https://github.com/microsoft/recommenders/blob/master/notebooks/04_model_select_and_optimize/nni_surprise_svd.ipynb) with NNI
    * [scikit-nni](https://github.com/ksachdeva/scikit-nni) Hyper-parameter search for scikit-learn pipelines using NNI
 * ### **Relevant Articles** ###
-  * [Hyper Parameter Optimization Comparison](docs/en_US/CommunitySharings/HpoComparision.md)
-  * [Neural Architecture Search Comparison](docs/en_US/CommunitySharings/NasComparision.md)
+  * [Hyper Parameter Optimization Comparison](docs/en_US/CommunitySharings/HpoComparison.md)
+  * [Neural Architecture Search Comparison](docs/en_US/CommunitySharings/NasComparison.md)
   * [Parallelizing a Sequential Algorithm TPE](docs/en_US/CommunitySharings/ParallelizingTpeSearch.md)
   * [Automatically tuning SVD with NNI](docs/en_US/CommunitySharings/RecommendersSvd.md)
   * [Automatically tuning SPTAG with NNI](docs/en_US/CommunitySharings/SptagAutoTune.md)

README_zh_CN.md

@@ -10,7 +10,7 @@
 
 **NNI (Neural Network Intelligence)** 是一个轻量但强大的工具包，帮助用户**自动**的进行[特征工程](docs/zh_CN/FeatureEngineering/Overview.md)，[神经网络架构搜索](docs/zh_CN/NAS/Overview.md)，[超参调优](docs/zh_CN/Tuner/BuiltinTuner.md)以及[模型压缩](docs/zh_CN/Compressor/Overview.md)。
 
-NNI 管理自动机器学习 (AutoML) 的 Experiment，**调度运行**由调优算法生成的 Trial 任务来找到最好的神经网络架构和/或超参，支持**各种训练环境**，如[本机](docs/zh_CN/TrainingService/LocalMode. md)，[远程服务器](docs/zh_CN/TrainingService/RemoteMachineMode. md)，[OpenPAI](docs/zh_CN/TrainingService/PaiMode. md)，[Kubeflow](docs/zh_CN/TrainingService/KubeflowMode. md)，[基于 K8S 的 FrameworkController（如，AKS 等)](docs/zh_CN/TrainingService/FrameworkControllerMode. md)， [DLWorkspace](docs/zh_CN/TrainingService/DLTSMode. md) (又称 DLTS)</a>, [AML](docs/zh_CN/TrainingService/AMLMode.md) (Azure Machine Learning) 以及其它环境。
+NNI 管理自动机器学习 (AutoML) 的 Experiment，**调度运行**由调优算法生成的 Trial 任务来找到最好的神经网络架构和/或超参，支持**各种训练环境**，如[本机](docs/zh_CN/TrainingService/LocalMode.md)，[远程服务器](docs/zh_CN/TrainingService/RemoteMachineMode.md)，[OpenPAI](docs/zh_CN/TrainingService/PaiMode.md)，[Kubeflow](docs/zh_CN/TrainingService/KubeflowMode.md)，[基于 K8S 的 FrameworkController（如，AKS 等)](docs/zh_CN/TrainingService/FrameworkControllerMode.md)， [DLWorkspace](docs/zh_CN/TrainingService/DLTSMode.md) (又称 DLTS)</a>, [AML](docs/zh_CN/TrainingService/AMLMode.md) (Azure Machine Learning) 以及其它环境。
 
 ## **使用场景**
 
@@ -328,8 +328,8 @@ You can use these commands to get more information about the experiment
    * 使用 NNI 的 [矩阵分解超参调优](https://github.com/microsoft/recommenders/blob/master/notebooks/04_model_select_and_optimize/nni_surprise_svd.ipynb)
    * [scikit-nni](https://github.com/ksachdeva/scikit-nni) 使用 NNI 为 scikit-learn 开发的超参搜索。
 * ### **相关文章** ### 
-   * [超参数优化的对比](docs/zh_CN/CommunitySharings/HpoComparision.md)
-   * [神经网络结构搜索的对比](docs/zh_CN/CommunitySharings/NasComparision.md)
+   * [超参数优化的对比](docs/zh_CN/CommunitySharings/HpoComparison.md)
+   * [神经网络结构搜索的对比](docs/zh_CN/CommunitySharings/NasComparison.md)
    * [并行化顺序算法：TPE](docs/zh_CN/CommunitySharings/ParallelizingTpeSearch.md)
    * [使用 NNI 为 SVD 自动调参](docs/zh_CN/CommunitySharings/RecommendersSvd.md)
    * [使用 NNI 为 SPTAG 自动调参](docs/zh_CN/CommunitySharings/SptagAutoTune.md)