sojiadeshina commented on a change in pull request #17050: Website edits
URL: https://github.com/apache/incubator-mxnet/pull/17050#discussion_r357341974
 
 

 ##########
 File path: docs/python_docs/python/tutorials/packages/autograd/index.md
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 @@ -39,7 +39,7 @@ Assuming we've calculated the gradient of each parameter 
with respect to the los
 
 We differentiate. [MXNet 
Gluon](/api/python/docs/tutorials/packages/gluon/index.html) uses Reverse Mode 
Automatic Differentiation (`autograd`) to backprogate gradients from the loss 
metric to the network parameters.
 
-![forward-backward](_static/autograd_forward_backward.png)
+![forward-backward](../../../../_static/autogluon_images/autograd_forward_backward.png)
 
 Review comment:
   another option for these might be putting them here: 
https://github.com/dmlc/web-data/tree/master/mxnet/tutorials

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