Merge pull request #108154 from craigcaseyMSFT/vcraic0319

fix broken links from CATS report

Author: PRMerger9

articles/cognitive-services/Speech-Service/includes/how-to/speech-to-text-basics/speech-to-text-basics-java.md

@@ -78,7 +78,7 @@ SpeechRecognizer recognizer = new SpeechRecognizer(config, audioConfig);
 The [Recognizer class](https://docs.microsoft.com/java/api/com.microsoft.cognitiveservices.speech.speechrecognizer?view=azure-java-stable) for the Speech SDK for Java exposes a few methods that you can use for speech recognition.
 
 * Single-shot recognition (async) - Performs recognition in a non-blocking (asynchronous) mode. This will recognize a single utterance. The end of a single utterance is determined by listening for silence at the end or until a maximum of 15 seconds of audio is processed.
-* Continuous recognition (async) - Asynchronously initiates continuous recognition operation. If you want to provide an audio file instead of using a microphone, you'll still need to provide an. To stop asynchronous continuous recognition, call [stopContinuousRecognitionAsync](https://docs.microsoft.com/java/api/com.microsoft.cognitiveservices.speech.speechrecognizer?view=azure-java-stable#stopcontinuousrecognitionasync).
+* Continuous recognition (async) - Asynchronously initiates continuous recognition operation. If you want to provide an audio file instead of using a microphone, you'll still need to provide an. To stop asynchronous continuous recognition, call [stopContinuousRecognitionAsync](https://docs.microsoft.com/java/api/com.microsoft.cognitiveservices.speech.speechrecognizer.stopcontinuousrecognitionasync).
 
 > [!NOTE]
 > Learn more about how to [choose a speech recognition mode](../../../how-to-choose-recognition-mode.md).
@@ -123,7 +123,7 @@ switch (result.getReason()) {
 
 ### Continuous recognition
 
-Continuous recognition is a bit more involved than single-shot recognition. It requires you to subscribe to the `recognizing`, `recognized`, and `canceled` events to get the recognition results. To stop recognition, you must call [`stopContinuousRecognitionAsync`](https://docs.microsoft.com/java/api/com.microsoft.cognitiveservices.speech.speechrecognizer?view=azure-java-stable#stopcontinuousrecognitionasync). Here's an example of how continuous recognition is performed on an audio input file.
+Continuous recognition is a bit more involved than single-shot recognition. It requires you to subscribe to the `recognizing`, `recognized`, and `canceled` events to get the recognition results. To stop recognition, you must call [`stopContinuousRecognitionAsync`](https://docs.microsoft.com/java/api/com.microsoft.cognitiveservices.speech.speechrecognizer.stopcontinuousrecognitionasync). Here's an example of how continuous recognition is performed on an audio input file.
 
 Let's start by defining the input and initializing a [`SpeechRecognizer`](https://docs.microsoft.com/java/api/com.microsoft.cognitiveservices.speech.speechrecognizer?view=azure-java-stable):
 
@@ -180,7 +180,7 @@ recognizer.sessionStopped.addEventListener((s, e) -> {
 });
 ```
 
-With everything set up, we can call [`stopContinuousRecognitionAsync`](https://docs.microsoft.com/java/api/com.microsoft.cognitiveservices.speech.speechrecognizer?view=azure-java-stable#startcontinuousrecognitionasync).
+With everything set up, we can call [`stopContinuousRecognitionAsync`](https://docs.microsoft.com/java/api/com.microsoft.cognitiveservices.speech.speechrecognizer.stopcontinuousrecognitionasync).
 
 ```java
 // Starts continuous recognition. Uses StopContinuousRecognitionAsync() to stop recognition.

articles/machine-learning/azure-machine-learning-release-notes.md

@@ -429,7 +429,7 @@ Access the following web-based authoring tools from the studio:
 ### Azure Machine Learning designer enhancements
 
 + Formerly known as the visual interface 
-+	11 new [modules](algorithm-module-reference/module-reference.md) including recommenders, classifiers, and training utilities including feature engineering, cross validation, and data transformation.
++    11 new [modules](algorithm-module-reference/module-reference.md) including recommenders, classifiers, and training utilities including feature engineering, cross validation, and data transformation.
 
 ### R SDK 
 
@@ -439,9 +439,9 @@ The Azure Machine Learning SDK for R uses the `reticulate` package to bind to th
 
 Main capabilities of the SDK include:
 
-+	Manage cloud resources for monitoring, logging, and organizing your machine learning experiments.
-+	Train models using cloud resources, including GPU-accelerated model training.
-+	Deploy your models as webservices on Azure Container Instances (ACI) and Azure Kubernetes Service (AKS).
++    Manage cloud resources for monitoring, logging, and organizing your machine learning experiments.
++    Train models using cloud resources, including GPU-accelerated model training.
++    Deploy your models as webservices on Azure Container Instances (ACI) and Azure Kubernetes Service (AKS).
 
 See the [package website](https://azure.github.io/azureml-sdk-for-r) for complete documentation.
 
@@ -462,28 +462,28 @@ Azure Machine Learning is now a resource provider for Event Grid, you can config
     + We are releasing preview support for disk encryption of your local SSD in Azure Machine Learning Compute. Please raise a technical support ticket to get your subscription whitelisted to use this feature.
     + Public Preview of Azure Machine Learning Batch Inference. Azure Machine Learning Batch Inference targets large inference jobs that are not time-sensitive. Batch Inference provides cost-effective inference compute scaling, with unparalleled throughput for asynchronous applications. It is optimized for high-throughput, fire-and-forget inference over large collections of data.
     + [**azureml-contrib-dataset**](https://docs.microsoft.com/python/api/azureml-contrib-dataset)
-    	+ Enabled functionalities for labeled dataset
-		```Python
-		import azureml.core
-		from azureml.core import Workspace, Datastore, Dataset
-		import azureml.contrib.dataset
-		from azureml.contrib.dataset import FileHandlingOption, LabeledDatasetTask
-
-		# create a labeled dataset by passing in your JSON lines file
-		dataset = Dataset._Labeled.from_json_lines(datastore.path('path/to/file.jsonl'), LabeledDatasetTask.IMAGE_CLASSIFICATION)
-
-		# download or mount the files in the `image_url` column
-		dataset.download()
-		dataset.mount()
-
-		# get a pandas dataframe
-		from azureml.data.dataset_type_definitions import FileHandlingOption
-		dataset.to_pandas_dataframe(FileHandlingOption.DOWNLOAD)
-		dataset.to_pandas_dataframe(FileHandlingOption.MOUNT)
-
-		# get a Torchvision dataset
-		dataset.to_torchvision()
-		```
+        + Enabled functionalities for labeled dataset
+        ```Python
+        import azureml.core
+        from azureml.core import Workspace, Datastore, Dataset
+        import azureml.contrib.dataset
+        from azureml.contrib.dataset import FileHandlingOption, LabeledDatasetTask
+
+        # create a labeled dataset by passing in your JSON lines file
+        dataset = Dataset._Labeled.from_json_lines(datastore.path('path/to/file.jsonl'), LabeledDatasetTask.IMAGE_CLASSIFICATION)
+
+        # download or mount the files in the `image_url` column
+        dataset.download()
+        dataset.mount()
+
+        # get a pandas dataframe
+        from azureml.data.dataset_type_definitions import FileHandlingOption
+        dataset.to_pandas_dataframe(FileHandlingOption.DOWNLOAD)
+        dataset.to_pandas_dataframe(FileHandlingOption.MOUNT)
+
+        # get a Torchvision dataset
+        dataset.to_torchvision()
+        ```
 
 + **Bug fixes and improvements**
   + **azure-cli-ml**
@@ -583,7 +583,7 @@ Azure Machine Learning is now a resource provider for Event Grid, you can config
     + Being able to consume `FileDataset` and `TabularDataset` as inputs to `PythonScriptStep`, `EstimatorStep`, and `HyperDriveStep` in Azure Machine Learning Pipeline
     + Performance of `FileDataset.mount` has been improved for folders with a large number of files
     + Being able to consume [FileDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.filedataset) and [TabularDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset) as inputs to [PythonScriptStep](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.python_script_step.pythonscriptstep), [EstimatorStep](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.estimatorstep), and [HyperDriveStep](https://docs.microsoft.com/python/api/azureml-pipeline-steps/azureml.pipeline.steps.hyperdrivestep) in the Azure Machine Learning Pipeline.
-    + Performance of FileDataset.[mount()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.filedataset#mount-mount-point-none-) has been improved for folders with a large number of files
+    + Performance of FileDataset.[mount()](https://docs.microsoft.com/python/api/azureml-core/azureml.data.filedataset#mount-mount-point-none----kwargs-) has been improved for folders with a large number of files
     + Added URL to known error recommendations in run details.
     + Fixed a bug in run.get_metrics where requests would fail if a run had too many children
     + Fixed a bug in [run.get_metrics](https://docs.microsoft.com/python/api/azureml-core/azureml.core.run.run#get-metrics-name-none--recursive-false--run-type-none--populate-false-) where requests would fail if a run had too many children
@@ -745,7 +745,7 @@ The Experiment tab in the [new workspace portal](https://ml.azure.com) has been
   + Enabled training with TabularDataset and FileDataset. Please visit https://aka.ms/dataset-tutorial for an example notebook.
 
   + **azureml-train-core**
-  	+ Added `Nccl` and `Gloo` support in PyTorch estimator
+      + Added `Nccl` and `Gloo` support in PyTorch estimator
 
 + **Bug fixes and improvements**
   + **azureml-automl-core**
@@ -782,9 +782,9 @@ The Experiment tab in the [new workspace portal](https://ml.azure.com) has been
     + Added dockerfile support in `environment_definition` parameter in estimators.
     + Simplified distributed training parameters in estimators.
 
-    	 ```py
-	    from azureml.train.dnn import TensorFlow, Mpi, ParameterServer
-	    ```
+         ```py
+        from azureml.train.dnn import TensorFlow, Mpi, ParameterServer
+        ```
 
 ## 2019-09-09
 
@@ -807,12 +807,12 @@ At the time of this release, the following browsers are supported: Chrome, Firef
 
 **Known issues:**
 
-1. Refresh your browser if you see “Something went wrong! Error loading chunk files” when deployment is in progress.
+1. Refresh your browser if you see "Something went wrong! Error loading chunk files" when deployment is in progress.
 
-1. Can’t delete or rename file in Notebooks and Files. During Public Preview you can use Jupyter UI or Terminal in Notebook VM to perform update file operations. Because it is a mounted network file system all changes you make on Notebook VM are immediately reflected in the Notebook Workspace.
+1. Can't delete or rename file in Notebooks and Files. During Public Preview you can use Jupyter UI or Terminal in Notebook VM to perform update file operations. Because it is a mounted network file system all changes you make on Notebook VM are immediately reflected in the Notebook Workspace.
 
 1. To SSH into the Notebook VM:
-   1. Find the SSH keys that were created during VM setup. Or, find the keys in the Azure Machine Learning workspace > open Compute tab > locate Notebook VM in the list > open it’s properties : copy the keys from the dialog.
+   1. Find the SSH keys that were created during VM setup. Or, find the keys in the Azure Machine Learning workspace > open Compute tab > locate Notebook VM in the list > open it's properties : copy the keys from the dialog.
    1. Import those public and private SSH keys to your local machine.
    1. Use them to SSH into the Notebook VM.
 
@@ -1036,7 +1036,7 @@ At the time of this release, the following browsers are supported: Chrome, Firef
     + Fixing a bug in the Ensemble selection procedure which was unnecessarily growing the resulting ensemble even if the scores remained constant.
     + Enable re-use of some featurizations across CV Splits for forecasting tasks. This speeds up the run-time of the setup run by roughly a factor of n_cross_validations for expensive featurizations like lags and rolling windows.
     + Addressing an issue if time is out of pandas supported time range. We now raise a DataException if time is less than pd.Timestamp.min or greater than pd.Timestamp.max
-    + Forecasting now allows different frequencies in train and test sets if they can be aligned. For example,  “quarterly starting in January” and at “quarterly starting in October” can be aligned.
+    + Forecasting now allows different frequencies in train and test sets if they can be aligned. For example,  "quarterly starting in January" and at "quarterly starting in October" can be aligned.
     + The property "parameters" was added to the TimeSeriesTransformer.
     + Remove old exception classes.
     + In forecasting tasks, the `target_lags` parameter now accepts a single integer value or a list of integers. If the integer was provided, only one lag will be created. If a list is provided, the unique values of lags will be taken. target_lags=[1, 2, 2, 4] will create lags of one, 2 and 4 periods.
@@ -1329,9 +1329,9 @@ In Azure portal, you can now:
 + Create and run automated ML experiments
 + Create a Notebook VM to try out sample Jupyter notebooks or your own.
 + Brand new Authoring section (Preview) in the Azure Machine Learning workspace, which includes Automated Machine Learning, Visual Interface and Hosted Notebook VMs
-	+ Automatically create a model using Automated machine learning
-	+ Use a drag and drop Visual Interface to run experiments
-	+ Create a Notebook VM to explore data, create models, and deploy services.
+    + Automatically create a model using Automated machine learning
+    + Use a drag and drop Visual Interface to run experiments
+    + Create a Notebook VM to explore data, create models, and deploy services.
 + Live chart and metric updating in run reports and run details pages
 + Updated file viewer for logs, outputs, and snapshots in Run details pages.
 + New and improved report creation experience in the Experiments tab.
@@ -1587,9 +1587,9 @@ Note: Data Prep Python SDK will no longer install `numpy` and `pandas` packages.
 
 + **Changes**:
   + Azure ML SDK no longer has azure-cli packages as dependency. Specifically, azure-cli-core and azure-cli-profile dependencies have been removed from azureml-core. These are the  user impacting changes:
-  	+ If you are performing "az login" and then using azureml-sdk, the SDK will do the browser or device code log in one more time. It won't use any credentials state created by "az login".
-	+ For Azure CLI authentication, such as using "az login", use _azureml.core.authentication.AzureCliAuthentication_ class. For Azure CLI authentication, do  _pip install azure-cli_ in the Python environment where you have installed azureml-sdk.
-	+ If you are doing "az login" using a service principal for automation, we recommend using _azureml.core.authentication.ServicePrincipalAuthentication_ class, as azureml-sdk won't use credentials state created by azure CLI.
+      + If you are performing "az login" and then using azureml-sdk, the SDK will do the browser or device code log in one more time. It won't use any credentials state created by "az login".
+    + For Azure CLI authentication, such as using "az login", use _azureml.core.authentication.AzureCliAuthentication_ class. For Azure CLI authentication, do  _pip install azure-cli_ in the Python environment where you have installed azureml-sdk.
+    + If you are doing "az login" using a service principal for automation, we recommend using _azureml.core.authentication.ServicePrincipalAuthentication_ class, as azureml-sdk won't use credentials state created by azure CLI.
 
 + **Bug fixes**: This release mostly contains minor bug fixes
 
@@ -1602,13 +1602,13 @@ Note: Data Prep Python SDK will no longer install `numpy` and `pandas` packages.
 ### Azure portal: new features
 + New drag and drop charting experience for reports. Users can drag a column or attribute from the well to the chart area where the system will automatically select an appropriate chart type for the user based on the type of data. Users can change the chart type to other applicable types or add additional attributes.
 
-	Supported Chart Types:
-	- Line Chart
-	- Histogram
-	- Stacked Bar Chart
-	- Box Plot
-	- Scatter Plot
-	- Bubble Plot
+    Supported Chart Types:
+    - Line Chart
+    - Histogram
+    - Stacked Bar Chart
+    - Box Plot
+    - Scatter Plot
+    - Bubble Plot
 + The portal now dynamically generates reports for experiments. When a user submits a run to an experiment, a report will automatically be generated with logged metrics and graphs to allow comparison across different runs.
 
 ## 2019-01-14
@@ -1701,7 +1701,7 @@ Azure Machine Learning Compute can be created in Python, using Azure portal, or
   * Additional clearer error messages
   * to_spark_dataframe() will no longer crash when Spark uses `Kryo` serialization
   * Value Count Inspector can now show more than 1000 unique values
-  * Random Split no longer fails if the original Dataflow doesn’t have a name
+  * Random Split no longer fails if the original Dataflow doesn't have a name
 
 + **More information**
   * [Azure Machine Learning Data Prep SDK](https://aka.ms/data-prep-sdk)
@@ -1798,7 +1798,7 @@ The Azure portal for Azure Machine Learning has the following updates:
   * String column type now shows up in metrics view
   * Type count now is fixed to show ValueKinds mapped to single FieldType instead of individual ones
   * Write_to_csv no longer fails when path is provided as a string
-  * When using Replace, leaving “find” blank will no longer fail
+  * When using Replace, leaving "find" blank will no longer fail
 
 ## 2018-10-12
 

articles/machine-learning/concept-environments.md

@@ -90,7 +90,7 @@ See the following diagram that shows three environment definitions. Two of them
 If you create an environment with unpinned package dependency, for example ```numpy```, that environment will keep using the package version installed at the time of environment creation. Also, any future environment with matching definition will keep using the old version. To update the package, specify a version number to force image rebuild, for example ```numpy==1.18.1```. Note that new dependencies, including nested ones will be installed that might break a previously working scenario
 
 > [!WARNING]
->  The [Environment.build](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment.environment?view=azure-ml-py#build-workspace-) method will rebuild the cached image, with possible side-effect of updating unpinned packages and breaking reproducibility for all environment definitions corresponding to that cached image.
+>  The [Environment.build](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment.environment?view=azure-ml-py#build-workspace--image-build-compute-none-) method will rebuild the cached image, with possible side-effect of updating unpinned packages and breaking reproducibility for all environment definitions corresponding to that cached image.
 
 ## Next steps
 

articles/machine-learning/how-to-create-register-datasets.md

@@ -151,7 +151,7 @@ datastore.upload(src_dir='data', target_path='data')
 dataset = Dataset.Tabular.from_delimited_files(datastore.path('data/prepared.csv'))
 ```
 
-Use the [`from_sql_query()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-sql-query-query--validate-true--set-column-types-none-) method on the `TabularDatasetFactory` class to read from Azure SQL Database:
+Use the [`from_sql_query()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#from-sql-query-query--validate-true--set-column-types-none--query-timeout-30-) method on the `TabularDatasetFactory` class to read from Azure SQL Database:
 
 ```Python