diff --git a/docs/modules/db.rst b/docs/modules/db.rst
index d90496a5a..0173ce5c2 100644
--- a/docs/modules/db.rst
+++ b/docs/modules/db.rst
@@ -11,12 +11,12 @@ If you have trouble, you can ask for help on `fangde.liu@imperial.ac.uk <fangde.
 Why TensorDB
 ----------------
 
-TensorLayer is designed for production, aiming to be applied large scale machine learning application. 
-TensorDB introduce the database infracture to address the many challenges in large scale machine learning project, such as: 
+TensorLayer is designed for production, aiming to apply large scale machine learning application. 
+TensorDB introduces the database infrastructure to address many challenges in the large scale machine learning project, such as: 
 
-1. How to mangage the training data and load the training datasets
+1. How to manage the training data and load the training datasets
 2. When the dataset is so large that beyonds the storage limitation of one computer
-3. How shoud we managment different models and version, and comparing different models.
+3. How shoud we manage different models and versions, and comparing them.
 4. How to automate the whole training, evaluaiton and deploy machine learning model automatically.
 
 In TensorLayer system, we introduce the database technology to the issues above.
@@ -26,7 +26,7 @@ TensorDB is designed by following three principles.
 Everything is Data
 ^^^^^^^^^^^^^^^^^^
 
-TensorDB is a data  warehouse that stores that capture the whole machine learning development process. the data inside tensordb can be catagloried as:
+TensorDB is a data warehouse that stores that capture the whole machine learning development process. the data inside tensordb can be catagloried as:
 
 1. Data and Labels: Which includes all the data for training, validation and prediction. The labels can be manually labelled or generated by machine
 2. Model Architecture: This group store the different model architecture, which user can select to use