Merge pull request #23 from MicrosoftDocs/master

hello newbies

Author: WilliamAntonRohm

.openpublishing.redirection.json

@@ -1905,11 +1905,6 @@
             "redirect_url": "/visualstudio/python/python-interactive-repl-in-visual-studio",
             "redirect_document_id": false
         },
-        {
-            "source_path": "docs/python/managing-python-on-azure-app-service.md.md",
-            "redirect_url": "/visualstudio/python/configure-web-apps-for-iis-windows",
-            "redirect_document_id": false
-        },        
         {
             "source_path": "docs/python/options.md",
             "redirect_url": "/visualstudio/python/python-support-options-and-settings-in-visual-studio",
@@ -6821,6 +6816,46 @@
             "source_path": "docs/vs-2015/extensibility/upgrading-custom-project-and-item-templates-for-visual-studio-%2215%22.md",
             "redirect_url": "/visualstudio/extensibility/upgrading-custom-project-and-item-templates-for-visual-studio-2017?view=vs-2015",
             "redirect_document_id": false
+        },
+        {
+            "source_path": "docs/cross-platform/visual-studio-and-xamarin.md",
+            "redirect_url": "/xamarin/",
+            "redirect_document_id": false
+        },
+        {
+            "source_path": "docs/cross-platform/setup-and-install.md",
+            "redirect_url": "/xamarin/cross-platform/get-started/installation/",
+            "redirect_document_id": false
+        },
+        {
+            "source_path": "docs/cross-platform/learn-about-mobile-development-with-xamarin.md",
+            "redirect_url": "/xamarin/cross-platform/get-started/introduction-to-mobile-development/",
+            "redirect_document_id": false
+        },
+        {
+            "source_path": "docs/cross-platform/verify-your-xamarin-environment.md",
+            "redirect_url": "/xamarin/cross-platform/get-started/installation/",
+            "redirect_document_id": false
+        },
+        {
+            "source_path": "docs/cross-platform/setup-install-and-verifications-for-mac-users.md",
+            "redirect_url": "/xamarin/cross-platform/get-started/installation/",
+            "redirect_document_id": false
+        },
+        {
+            "source_path": "docs/cross-platform/learn-app-building-basics-with-xamarin-forms-in-visual-studio.md",
+            "redirect_url": "/xamarin/xamarin-forms/get-started/first-app/",
+            "redirect_document_id": false
+        },
+        {
+            "source_path": "docs/cross-platform/build-apps-with-native-ui-using-xamarin-in-visual-studio.md",
+            "redirect_url": "/xamarin/cross-platform/get-started/",
+            "redirect_document_id": false
+        },
+        {
+            "source_path": "docs/cross-platform/application-lifecycle-management-alm-with-xamarin-apps.md",
+            "redirect_url": "/xamarin/tools/ci/devops/",
+            "redirect_document_id": true
         }
     ]
 }

connected-environment/docfx.json

@@ -40,11 +40,12 @@
       "feedback_product_url": "https://aka.ms/vsce-product-survey",
       "breadcrumb_path": "/visualstudio/connected-environment/breadcrumb/toc.json",
       "extendBreadcrumb": true,
-      "ROBOTS":  "NOINDEX,NOFOLLOW",
+      "ROBOTS": "NOINDEX,NOFOLLOW",
       "titleSuffix": "Visual Studio Connected Environment"
     },
     "fileMetadata": {},
     "template": [],
-    "dest": "connected-environment"
+    "dest": "connected-environment",
+    "markdownEngineName": "markdig"
   }
 }

docs/ai/about-ai-tools.md

@@ -23,19 +23,19 @@ Get started with deep learning using [Microsoft Cognitive Toolkit (CNTK)](http:/
 ## Develop, debug and deploy deep learning models and AI solutions
 Use the productivity features of Visual Studio to accelerate AI innovation today. Use built-in code editor features like syntax highlighting, IntelliSense and text auto formatting. You can interactively test your deep learning application in your local environment using step-through debugging on local variables and models.
 
-![deep learning ide](media\about\ide.png)
+![deep learning ide](media/about/ide.png)
 
 ## Get started quickly with the Azure Machine Learning Sample Gallery
 Visual Studio Tools for AI is integrated with Azure Machine Learning to make it easy to browse through a gallery of sample experiments using CNTK, TensorFlow, MMLSpark and more.
 
-![sample explorer](media\about\gallery.png)
+![sample explorer](media/about/gallery.png)
 
 [Learn more about creating projects from the sample gallery](create-project-gallery.md)
 
 ## Scale out deep learning model training and/or inferencing to the cloud
 This extension makes it easy to train models on your local computer or you can submit jobs to the cloud by using our integration with Azure Machine Learning. You can submit jobs to different compute targets like Spark clusters, Azure GPU virtual machines and more
 
-![submit job](media\about\submitjobs.png)
+![submit job](media/about/submitjobs.png)
 
 [Learn more about training models in the cloud](tensorflow-vm.md)
 

docs/ai/create-project-existing.md

@@ -6,28 +6,27 @@ ms.technology: vs-ai-tools
 Once you've [installed Visual Studio Tools for AI](installation.md), it's easy to bring existing Python code into a Visual Studio project.
 
 > [!Important]
->
 > The process described here does not move or copy the original source files. If you want to work with a copy, duplicate the folder first.
 
 1. Launch Visual Studio and select **File > New > Project**.
 
-1. In the **New Project** dialog, search for "**AI Tools**", select the "**From Existing Python code**" template, give the project a name and location, and select **OK**.
+2. In the **New Project** dialog, search for "**AI Tools**", select the "**From Existing Python code**" template, give the project a name and location, and select **OK**.
 
-    ![New Project from Existing Code, step 1](media\create-project-existing\new-ai-project.png)
+   ![New Project from Existing Code, step 1](media/create-project-existing/new-ai-project.png)
 
-1. In the wizard that appears, set the path to your existing code, set a filter for file types, and specify any search paths that your project requires, then select **OK**. If you don't know what search paths are, leave that field blank.
+3. In the wizard that appears, set the path to your existing code, set a filter for file types, and specify any search paths that your project requires, then select **OK**. If you don't know what search paths are, leave that field blank.
 
-![New Project from Existing Code, step 2](media\create-project-existing\azurebatch-newproject.png)
+   ![New Project from Existing Code, step 2](media/create-project-existing/azurebatch-newproject.png)
 
-> If your existing code is part of an Azure Machine Learning project, check the "**Is Azure Machine Learning folder**" to ensure successful conversion of important Azure Machine Learning configuration details like which Experimentation account, which Workspace, the compute contexts to use and more.
+   If your existing code is part of an Azure Machine Learning project, check the **Is Azure Machine Learning folder** to ensure successful conversion of important Azure Machine Learning configuration details like the Experimentation account, Workspace, which compute contexts to use, and more.
 
-1. To set a startup file, locate the file in Solution Explorer, right-click, and select **Set as Startup File**.
+4. To set a startup file, locate the file in **Solution Explorer**, right-click, and select **Set as Startup File**.
 
-1. If desired, run the program by pressing Ctrl+F5 or selecting **Debug > Start Without Debugging**.
+5. Run the program by pressing **Ctrl**+**F5** or selecting **Debug > Start Without Debugging**.
 
 > [!div class="nextstepaction"]
 > [Tutorial: Working with Python in Visual Studio](../python/tutorial-working-with-python-in-visual-studio-step-00-installation.md)
 
-## See Also
+## See also
 
 - [Manually identify an existing Python environment](../python/managing-python-environments-in-visual-studio.md#manually-identify-an-existing-environment)

docs/ai/create-project-gallery.md

@@ -25,36 +25,36 @@ Once you've [installed Visual Studio Tools for AI](installation.md), it's easy t
 
 1. Launch Visual Studio. Open the **Server Explorer** by opening the **AI Tools** menu and choosing **Select Cluster**
 
-    ![Cluster chooser](media\create-project-gallery\select-cluster.png)
+    ![Cluster chooser](media/create-project-gallery/select-cluster.png)
 
-1. Sign in to your Azure Machine Learning subscription by right-clicking the **Azure Machine Learning** node in the Server Explorer then select **Login** and follow the directions.
+2. Sign in to your Azure Machine Learning subscription by right-clicking the **Azure Machine Learning** node in the Server Explorer then select **Login** and follow the directions.
 
-    ![login](media\create-project-gallery\azureml-login.png)
+    ![login](media/create-project-gallery/azureml-login.png)
 
-2. Select **AI Tools > Azure Machine Learning Sample Gallery**.
+3. Select **AI Tools > Azure Machine Learning Sample Gallery**.
 
-    ![Sample gallery](media\create-project-gallery\gallery.png)
+    ![Sample gallery](media/create-project-gallery/gallery.png)
 
-1. For this Quickstart, select the "**MNIST using TensorFlow**" sample and click **Install**. Provide the following:
+4. For this Quickstart, select the "**MNIST using TensorFlow**" sample and click **Install**. Provide the following:
 
- - **Resource Group**: Azure resource group where your metadata will be stored
- - **Account**: Azure Machine Learning experimentation Account
- - **Workspace**: Azure Machine Learning workspace
- - **Project Type**: The machine learning framework. In this case choose **TensorFlow**
- - **Add to Solution**: determines whether to add to your current Visual Studio Solution or a create and open a new solution
- - **Project Path**: Location to save the code
- - **Project Name**: Type **TensorFlowMNIST**
+   - **Resource Group**: Azure resource group where your metadata will be stored
+   - **Account**: Azure Machine Learning experimentation Account
+   - **Workspace**: Azure Machine Learning workspace
+   - **Project Type**: The machine learning framework. In this case choose **TensorFlow**
+   - **Add to Solution**: determines whether to add to your current Visual Studio Solution or a create and open a new solution
+   - **Project Path**: Location to save the code
+   - **Project Name**: Type **TensorFlowMNIST**
 
-![Resulting project when using the Python Application template](media/create-project-gallery/new-AzureSampleProject.png)
+   ![Resulting project when using the Python Application template](media/create-project-gallery/new-AzureSampleProject.png)
 
-1. Visual Studio creates the project file (a `.pyproj` file on disk) along with other files defined in the sample. With the "MNIST" template, the project contains several files.
+5. Visual Studio creates the project file (a `.pyproj` file on disk) along with other files defined in the sample. With the "MNIST" template, the project contains several files.
 
-    ![mnist](media\create-project-gallery\azml-mnist.png)
+    ![mnist](media/create-project-gallery/azml-mnist.png)
 
-1. Submit the job to Azure Machine Learning.
+6. Submit the job to Azure Machine Learning.
 
-    ![mnist](media\create-project-gallery\submit-azml.png)
+    ![mnist](media/create-project-gallery/submit-azml.png)
 
-1. Run in a Docker container or on your local machine
+7. Run in a Docker container or on your local machine
 
-    ![mnist](media\create-project-gallery\azml-local.png)
+    ![mnist](media/create-project-gallery/azml-local.png)

docs/ai/create-project-repo.md

@@ -7,13 +7,13 @@ Once you've [installed Visual Studio Tools for AI](installation.md), you can eas
 
 1. To connect to GitHub repositories, run the Visual Studio installer, select **Modify**, and select the **Individual components** tab. Scroll down to the **Code tools** section, select **GitHub extension for Visual Studio**, and select **Modify**.
 
-    ![Selecting the GitHub extension in the Visual Studio installer](media\create-project-repo\installation-github-extension.png)
+    ![Selecting the GitHub extension in the Visual Studio installer](media/create-project-repo/installation-github-extension.png)
 
 2. Launch Visual Studio.
 
 3. Select **View > Team Explorer...** to open the **Team Explorer** window in which you can connect to GitHub or Azure DevOps, or clone a repository.
 
-    ![Team explorer window showing Azure DevOps, GitHub, and cloning a repository](media\create-project-repo\team-explorer.png)
+    ![Team explorer window showing Azure DevOps, GitHub, and cloning a repository](media/create-project-repo/team-explorer.png)
 
 4. In the URL field under **Local Git Repositories**, enter `https://github.com/Microsoft/samples-for-ai`, enter a folder for the cloned files, and select **Clone**.
 
@@ -22,27 +22,27 @@ Once you've [installed Visual Studio Tools for AI](installation.md), you can eas
 
 5. When cloning is complete, double-click the repository folder at the bottom of Team Explorer to navigate to the repository dashboard. Under **Solutions**, select **New...**.
 
-    ![Team explorer window, creating a new project from a clone](media\create-project-repo\team-explorer-new-project.png)
+    ![Team explorer window, creating a new project from a clone](media/create-project-repo/team-explorer-new-project.png)
 
 6. In the **New Project** dialog that appears, select "**From Existing Python Code**", specify a name for the project, set **Location** to the same folder as the repository, and select **OK**. In the wizard that appears, select **Finish**.
 
 7. Select **View > Solution Explorer** from the menu.
 
 8. In Solution Explorer, expand the `TensorFlow Examples> MNIST` node, right-click `convolutional.py`, and select **Set as Startup File**. This step tells Visual Studio which file it should use when running the project.
 
-10. Press Ctrl+F5 or select **Debug > Start Without Debugging** to run the program. If you see an `, re-check the working directory setting in the previous step.
+9. Press **Ctrl**+**F5** or select **Debug > Start Without Debugging** to run the program. If you see an `, re-check the working directory setting in the previous step.
 
+10. When the program runs successfully, you'll see it start to download your training and test dataset, then train the model and output your error rate. You want error rate to decrease over time
 
-11. When the program runs successfully, you'll see it start to download your training and test dataset, then train the model and output your error rate. You want error rate to decrease over time
+    ![First output from the Python MNIST program](media/create-project-repo/tensorflow-mnist-running.png)
 
-    ![First output from the Python MNIST program](media\create-project-repo\tensorflow-mnist-running.png)
-
-> If you are using Anaconda and get an error about missing numpy, you may need to [change your Python environment to use Anaconda](../python/selecting-a-python-environment-for-a-project.md).
+   > [!NOTE]
+   > If you're using Anaconda and get an error about missing numpy, you may need to [change your Python environment to use Anaconda](../python/selecting-a-python-environment-for-a-project.md).
 
 11. You can visualize the progress with TensorBoard. Right click your project and click **Run TensorBoard** then select the directory of your output TensorBoard logs.
 
-    ![run tensorboard](media\create-project-repo\run-tensorboard.png)
+   ![run tensorboard](media/create-project-repo/run-tensorboard.png)
 
-11. Notice the error decreasing overtime, which means the quality is improving
+12. Notice the error decreasing overtime, which means the quality is improving
 
-    ![run tensorboard](media\create-project-repo\tensorboard.png)
+   ![run tensorboard](media/create-project-repo/tensorboard.png)

docs/ai/create-project.md

@@ -7,24 +7,24 @@ Once you've [installed Visual Studio Tools for AI](installation.md), it's easy t
 
 1. Launch Visual Studio.
 
-1. Select **File > New > Project** (Ctrl+Shift+N). In the **New Project** dialog, search for "**AI Tools**", and select the template you want. Note that selecting a template displays a short description of what the template provides.
+2. Select **File > New > Project** (Ctrl+Shift+N). In the **New Project** dialog, search for "**AI Tools**", and select the template you want. Note that selecting a template displays a short description of what the template provides.
 
-    ![VS2017 New Project dialog with Python template](media\create-project\new-ai-project.png)
+    ![VS2017 New Project dialog with Python template](media/create-project/new-ai-project.png)
 
-1. For this Quickstart, select the "**TensorFlow Application**" template, give the project a name (such as "MNIST") and location, and select **OK**.
+3. For this Quickstart, select the "**TensorFlow Application**" template, give the project a name (such as "MNIST") and location, and select **OK**.
 
-1. Visual Studio creates the project file (a `.pyproj` file on disk) along with any other files as described by the template. With the "TensorFlow Application" template, the project contains one file named the same as your project. The file is open in the Visual Studio editor by default.
+4. Visual Studio creates the project file (a `.pyproj` file on disk) along with any other files as described by the template. With the "TensorFlow Application" template, the project contains one file named the same as your project. The file is open in the Visual Studio editor by default.
 
-    ![Resulting project when using the Python Application template](media\create-project\new-tensorflowapp.png)
+    ![Resulting project when using the Python Application template](media/create-project/new-tensorflowapp.png)
 
-1. Notice the code already imports several libraries including TensorFlow, numpy, sys and os. Additionally it starts your application ready with some input arguments to easily enable switching the location of input training data, output models and log files. These params are useful when you submit your jobs to multiple compute contexts (ie different directory on your local dev box than on an Azure File Share).
+5. Notice the code already imports several libraries including TensorFlow, numpy, sys and os. Additionally it starts your application ready with some input arguments to easily enable switching the location of input training data, output models and log files. These params are useful when you submit your jobs to multiple compute contexts (ie different directory on your local dev box than on an Azure File Share).
 
-1. Your project also has some properties created to make it easy to debug your app by automatically passing commandline arguments to these input parameters. **Right click** your project then select **Properties**
+6. Your project also has some properties created to make it easy to debug your app by automatically passing commandline arguments to these input parameters. **Right click** your project then select **Properties**
 
-    ![Properties](media\create-project\project-properties.png)
+    ![Properties](media/create-project/project-properties.png)
 
-1. Click the **Debug** tab to see the Script Arguments automatically added. you may change them as needed to where your input data is located and where you would like your output stored.
+7. Click the **Debug** tab to see the Script Arguments automatically added. you may change them as needed to where your input data is located and where you would like your output stored.
 
-	![Properties](media\create-project\/project-properties_1.png)
+    ![Properties](media/create-project//project-properties_1.png)
 
-1. Run the program by pressing Ctrl+F5 or selecting **Debug > Start Without Debugging** on the menu. The results are displayed in a console window.
+8. Run the program by pressing Ctrl+F5 or selecting **Debug > Start Without Debugging** on the menu. The results are displayed in a console window.