Update invalid links.

README.md

@@ -31,7 +31,7 @@ enrichment of the AI software/hardware application ecosystem.
 
 <img src="docs/MindSpore-architecture.png" alt="MindSpore Architecture" width="600"/>
 
-For more details please check out our [Architecture Guide](https://www.mindspore.cn/doc/note/en/master/design/mindspore/architecture.html) (visit [Architecture Guide](https://www.mindspore.cn/docs/en/master/architecture.html) before Sep. 24).
+For more details please check out our [Architecture Guide](https://www.mindspore.cn/doc/note/en/master/design/mindspore/architecture.html).
 
 ### Automatic Differentiation
 
@@ -208,7 +208,7 @@ please check out [docker](docker/README.md) repo for the details.
 
 ## Quickstart
 
-See the [Quick Start](https://www.mindspore.cn/tutorial/training/en/master/quick_start/quick_start.html) (visit [Quick Start](https://www.mindspore.cn/tutorial/en/master/quick_start/quick_start.html) before Sep. 24)
+See the [Quick Start](https://www.mindspore.cn/tutorial/training/en/master/quick_start/quick_start.html) 
 to implement the image classification.
 
 ## Docs

README_CN.md

@@ -28,7 +28,7 @@ MindSpore提供了友好的设计和高效的执行，旨在提升数据科学
 
 <img src="docs/MindSpore-architecture.png" alt="MindSpore Architecture" width="600"/>
 
-欲了解更多详情，请查看我们的[总体架构](https://www.mindspore.cn/doc/note/zh-CN/master/design/mindspore/architecture.html)（9月24日前请访问[总体架构](https://www.mindspore.cn/docs/zh-CN/master/architecture.html)）。
+欲了解更多详情，请查看我们的[总体架构](https://www.mindspore.cn/doc/note/zh-CN/master/design/mindspore/architecture.html)。
 
 ### 自动微分
 
@@ -203,7 +203,7 @@ MindSpore的Docker镜像托管在[Docker Hub](https://hub.docker.com/r/mindspore
 
 ## 快速入门
 
-参考[快速入门](https://www.mindspore.cn/tutorial/training/zh-CN/master/quick_start/quick_start.html)（9月24日前请访问[快速入门](https://www.mindspore.cn/tutorial/zh-CN/master/quick_start/quick_start.html)）实现图片分类。
+参考[快速入门](https://www.mindspore.cn/tutorial/training/zh-CN/master/quick_start/quick_start.html)实现图片分类。
 
 
 ## 文档

model_zoo/official/cv/deeplabv3/README.md

@@ -60,7 +60,7 @@ Pascal VOC datasets and Semantic Boundaries Dataset
 
 ## Mixed Precision
 
-The [mixed precision](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data types, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
+The [mixed precision](https://www.mindspore.cn/tutorial/training/zh-CN/master/advanced_use/enable_mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data types, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware.
 For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’.
 
 # [Environment Requirements](#contents)
@@ -70,8 +70,8 @@ For FP16 operators, if the input data type is FP32, the backend of MindSpore wil
 - Framework
   - [MindSpore](https://www.mindspore.cn/install/en)
 - For more information, please check the resources below：
-  - [MindSpore tutorials](https://www.mindspore.cn/tutorial/zh-CN/master/index.html)
-  - [MindSpore API](https://www.mindspore.cn/api/zh-CN/master/index.html)
+  - [MindSpore Tutorials](https://www.mindspore.cn/tutorial/training/zh-CN/master/index.html)
+  - [MindSpore Python API](https://www.mindspore.cn/doc/api_python/zh-CN/master/index.html)
 - Install python packages in requirements.txt
 - Generate config json file for 8pcs training
   

model_zoo/official/cv/yolov3_resnet18/README.md

@@ -176,7 +176,7 @@ Note the results is two-classification(person and face) used our own annotations
 ## [Evaluation Process](#contents)
 ### Evaluation on Ascend
 
-To eval, run `eval.py` with the dataset `image_dir`, `anno_path`(eval txt), `mindrecord_dir` and `ckpt_path`. `ckpt_path` is the path of [checkpoint](https://www.mindspore.cn/tutorial/training/en/master/use/save_and_load_model.html) file.
+To eval, run `eval.py` with the dataset `image_dir`, `anno_path`(eval txt), `mindrecord_dir` and `ckpt_path`. `ckpt_path` is the path of [checkpoint](https://www.mindspore.cn/tutorial/training/en/master/use/save_model.html) file.
 
   ```
   sh run_eval.sh 0 yolo.ckpt ./Mindrecord_eval ./dataset ./dataset/eval.txt

model_zoo/official/nlp/mass/README.md

@@ -633,4 +633,4 @@ The model has been validated on Ascend environment, not validated on CPU and GPU
 
 
 # ModelZoo Homepage  
- [Link](https://gitee.com/mindspore/mindspore/tree/master/mindspore/model_zoo)
+ [Link](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)