diff --git a/beginner_source/deeplabv3_on_android.rst b/beginner_source/deeplabv3_on_android.rst
index 25597b82dae..9b17e04fd7b 100644
--- a/beginner_source/deeplabv3_on_android.rst
+++ b/beginner_source/deeplabv3_on_android.rst
@@ -12,7 +12,7 @@ Semantic image segmentation is a computer vision task that uses semantic labels
 
 In this tutorial, we will provide a step-by-step guide on how to prepare and run the PyTorch DeepLabV3 model on Android, taking you from the beginning of having a model you may want to use on Android to the end of having a complete Android app using the model. We will also cover practical and general tips on how to check if your next favorable pre-trained PyTorch models can run on Android, and how to avoid pitfalls.
 
-.. note:: Before going through this tutorial, you should check out `PyTorch Mobile for Android <https://pytorch.org/mobile/android/>`_ and give the PyTorch Android `HelloWorld <https://github.com/pytorch/android-demo-app/tree/master/HelloWorldApp>`_ example app a quick try. This tutorial will go beyond the image classification model, usually the first kind of model deployed on mobile. The complete code repo for this tutorial is available `here <https://github.com/pytorch/android-demo-app/ImageSegmentation>`_.
+.. note:: Before going through this tutorial, you should check out `PyTorch Mobile for Android <https://pytorch.org/mobile/android/>`_ and give the PyTorch Android `HelloWorld <https://github.com/pytorch/android-demo-app/tree/master/HelloWorldApp>`_ example app a quick try. This tutorial will go beyond the image classification model, usually the first kind of model deployed on mobile. The complete code repo for this tutorial is available `here <https://github.com/pytorch/android-demo-app/tree/master/ImageSegmentation>`_.
 
 Learning Objectives
 -------------------
@@ -217,7 +217,7 @@ Now when you run the app on an Android emulator or preferably an actual device,
 Recap
 --------
 
-In this tutorial, we described what it takes to convert a pre-trained PyTorch DeepLabV3 model for Android and how to make sure the model can run successfully on Android. Our focus was to help you understand the process of confirming that a model can indeed run on Android. The complete code repo is available `here <https://github.com/pytorch/android-demo-app/ImageSegmentation>`_.
+In this tutorial, we described what it takes to convert a pre-trained PyTorch DeepLabV3 model for Android and how to make sure the model can run successfully on Android. Our focus was to help you understand the process of confirming that a model can indeed run on Android. The complete code repo is available `here <https://github.com/pytorch/android-demo-app/tree/master/ImageSegmentation>`_.
 
 More advanced topics such as quantization and using models via transfer learning or of your own on Android will be covered soon in future demo apps and tutorials.
 
diff --git a/beginner_source/deeplabv3_on_ios.rst b/beginner_source/deeplabv3_on_ios.rst
index 028f7373e09..ee27384ded6 100644
--- a/beginner_source/deeplabv3_on_ios.rst
+++ b/beginner_source/deeplabv3_on_ios.rst
@@ -12,7 +12,7 @@ Semantic image segmentation is a computer vision task that uses semantic labels
 
 In this tutorial, we will provide a step-by-step guide on how to prepare and run the PyTorch DeepLabV3 model on iOS, taking you from the beginning of having a model you may want to use on iOS to the end of having a complete iOS app using the model. We will also cover practical and general tips on how to check if your next favorite pre-trained PyTorch models can run on iOS, and how to avoid pitfalls.
 
-.. note:: Before going through this tutorial, you should check out `PyTorch Mobile for iOS <https://pytorch.org/mobile/ios/>`_ and give the PyTorch iOS `HelloWorld <https://github.com/pytorch/ios-demo-app/tree/master/HelloWorld>`_ example app a quick try. This tutorial will go beyond the image classification model, usually the first kind of model deployed on mobile. The complete code repo for this tutorial is available `here <https://github.com/pytorch/ios-demo-app/ImageSegmentation>`_.
+.. note:: Before going through this tutorial, you should check out `PyTorch Mobile for iOS <https://pytorch.org/mobile/ios/>`_ and give the PyTorch iOS `HelloWorld <https://github.com/pytorch/ios-demo-app/tree/master/HelloWorld>`_ example app a quick try. This tutorial will go beyond the image classification model, usually the first kind of model deployed on mobile. The complete code repo for this tutorial is available `here <https://github.com/pytorch/ios-demo-app/tree/master/ImageSegmentation>`_.
 
 Learning Objectives
 -------------------
@@ -236,7 +236,7 @@ Now when you run the app on an iOS simulator or an actual iOS device, you will s
 Recap
 --------
 
-In this tutorial, we described what it takes to convert a pre-trained PyTorch DeepLabV3 model for iOS and how to make sure the model can run successfully on iOS. Our focus was to help you understand the process of confirming that a model can indeed run on iOS. The complete code repo is available `here <https://github.com/pytorch/ios-demo-app/ImageSegmentation>`_.
+In this tutorial, we described what it takes to convert a pre-trained PyTorch DeepLabV3 model for iOS and how to make sure the model can run successfully on iOS. Our focus was to help you understand the process of confirming that a model can indeed run on iOS. The complete code repo is available `here <https://github.com/pytorch/ios-demo-app/tree/master/ImageSegmentation>`_.
 
 More advanced topics such as quantization and using models via transfer learning or of your own on iOS will be covered soon in future demo apps and tutorials.