modify quick_start

tutorials/source_en/quick_start/quick_start.md

@@ -316,8 +316,8 @@ if __name__ == "__main__":
 
 ### Saving the Configured Model
 
-MindSpore provides the callback mechanism to execute customized logic during training. `ModelCheckpoint` and `LossMonitor` provided by the framework are used in this example.
-`ModelCheckpoint` can save network models and parameters for subsequent fine-tuning. `LossMonitor` can monitor the changes of the `loss` value during training.
+MindSpore provides the callback mechanism to execute customized logic during training. `ModelCheckpoint` provided by the framework is used in this example.
+`ModelCheckpoint` can save network models and parameters for subsequent fine-tuning.
 
 ```python
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
@@ -333,7 +333,7 @@ if __name__ == "__main__":
 
 ### Configuring the Network Training
 
-Use the `model.train` API provided by MindSpore to easily train the network.
+Use the `model.train` API provided by MindSpore to easily train the network. `LossMonitor` can monitor the changes of the `loss` value during training.
 In this example, set `epoch_size` to 1 to train the dataset for five iterations.
 
 ```python

tutorials/source_zh_cn/quick_start/quick_start.md

@@ -317,8 +317,8 @@ if __name__ == "__main__":
 
 ### 配置模型保存
 
-MindSpore提供了callback机制，可以在训练过程中执行自定义逻辑，这里使用框架提供的`ModelCheckpoint`和`LossMonitor`为例。
-`ModelCheckpoint`可以保存网络模型和参数，以便进行后续的fine-tuning（微调）操作，`LossMonitor`可以监控训练过程中`loss`值的变化。
+MindSpore提供了callback机制，可以在训练过程中执行自定义逻辑，这里使用框架提供的`ModelCheckpoint`为例。
+`ModelCheckpoint`可以保存网络模型和参数，以便进行后续的fine-tuning（微调）操作。
 
 ```python
 from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
@@ -334,7 +334,7 @@ if __name__ == "__main__":
 
 ### 配置训练网络
 
-通过MindSpore提供的`model.train`接口可以方便地进行网络的训练。
+通过MindSpore提供的`model.train`接口可以方便地进行网络的训练。`LossMonitor`可以监控训练过程中`loss`值的变化。
 这里把`epoch_size`设置为1，对数据集进行1个迭代的训练。