TEChopra1000 commented on a change in pull request #16500: Fixing broken links
URL: https://github.com/apache/incubator-mxnet/pull/16500#discussion_r335597022
 
 

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 File path: docs/python_docs/python/tutorials/packages/autograd/index.md
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 @@ -196,7 +196,7 @@ print(x.grad)
 
 ## Advanced: Using Python control flow
 
-As mentioned before, one of the main advantages of `autograd` is the ability 
to automatically calculate gradients of dynamic graphs (i.e. graphs where the 
operators could be different on every forward pass). One example of this would 
be applying a tree structured recurrent network to parse a sentence using its 
parse tree. And we can use Python control flow operators to create a dynamic 
flow that depends on the data, rather than using [MXNet's control flow 
operators](https://mxnet.apache.org/versions/master/tutorials/control_flow/ControlFlowTutorial.html).
+As mentioned before, one of the main advantages of `autograd` is the ability 
to automatically calculate gradients of dynamic graphs (i.e. graphs where the 
operators could be different on every forward pass). One example of this would 
be applying a tree structured recurrent network to parse a sentence using its 
parse tree. And we can use Python control flow operators to create a dynamic 
flow that depends on the data, rather than using [MXNet's control flow 
operators](/api/python/tutorials/extend/control_flow.html).
 
 Review comment:
   ```suggestion
   As mentioned before, one of the main advantages of `autograd` is the ability 
to automatically calculate gradients of dynamic graphs (i.e. graphs where the 
operators could be different on every forward pass). One example of this would 
be applying a tree structured recurrent network to parse a sentence using its 
parse tree. And we can use Python control flow operators to create a dynamic 
flow that depends on the data, rather than using [MXNet's control flow 
operators](/api/python/docs/tutorials/packages/autograd/index.html#Advanced:-Using-Python-control-flow).
   ```

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