diff --git a/tutorials/source_en/beginner/autograd.md b/tutorials/source_en/beginner/autograd.md
index 6fead1c1c05ce181726eb7a3791320bcc8879d0c..4fc41ed0172053675ba3dba3ac76c1058abba86c 100644
--- a/tutorials/source_en/beginner/autograd.md
+++ b/tutorials/source_en/beginner/autograd.md
@@ -14,10 +14,8 @@ This chapter uses `ops.GradOperation` in MindSpore to find first-order derivativ
 
 ## First-order Derivative of the Input
 
-The formula needs to be defined before the input can be derived:
-$$
-f(x)=wx+b \tag {1}
-$$
+The formula needs to be defined before the input can be derived: $f(x)=wx+b \tag {1}$
+
 The example code below is an expression of Equation (1), and since MindSpore is functionally programmed, all expressions of computational formulas are represented as functions.
 
 ```python
@@ -36,10 +34,7 @@ class Net(nn.Cell):
         return f
 ```
 
-Define the derivative class `GradNet`. In the `__init__` function, define the `self.net` and `ops.GradOperation` networks. In the `construct` function, compute the derivative of `self.net`. Its corresponding MindSpore internally produces the following formula (2):
-$$
-f^{'}(x)=w\tag {2}
-$$
+Define the derivative class `GradNet`. In the `__init__` function, define the `self.net` and `ops.GradOperation` networks. In the `construct` function, compute the derivative of `self.net`. Its corresponding MindSpore internally produces the following formula (2): $f^{'}(x)=w\tag {2}$
 
 ```python
 from mindspore import dtype as mstype