update samples from Release-144 as a part of 1.0.60 SDK release

tutorials/img-classification-part1-training.ipynb

@@ -93,7 +93,7 @@
       "source": [
         "# load workspace configuration from the config.json file in the current folder.\n",
         "ws = Workspace.from_config()\n",
-        "print(ws.name, ws.location, ws.resource_group, ws.location, sep='\\t')"
+        "print(ws.name, ws.location, ws.resource_group, sep='\\t')"
       ]
     },
     {
@@ -125,10 +125,10 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "### Create or attach existing compute target\n",
+        "### Create or Attach existing compute resource\n",
         "By using Azure Machine Learning Compute, a managed service, data scientists can train machine learning models on clusters of Azure virtual machines. Examples include VMs with GPU support. In this tutorial, you create Azure Machine Learning Compute as your training environment. The code below creates the compute clusters for you if they don't already exist in your workspace.\n",
         "\n",
-        "**Creation of compute target takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace the code will skip the creation process."
+        "**Creation of compute takes approximately 5 minutes.** If the AmlCompute with that name is already in your workspace the code will skip the creation process."
       ]
     },
     {
@@ -258,9 +258,9 @@
         "\n",
         "### Upload data to the cloud\n",
         "\n",
-        "You downloaded and used the training data on the computer your notebook is running on.  In the next section, you will train a model on the remote Azure Machine Learning Compute.  The remote compute resource will also need access to your data. To provide access, upload your data to a centralized datastore associated with your workspace. This datastore provides fast access to your data when using remote compute targets in the cloud, as it is in the Azure data center.\n",
+        "Now make the data accessible remotely by uploading that data from your local machine into Azure so it can be accessed for remote training. The datastore is a convenient construct associated with your workspace for you to upload/download data, and interact with it from your remote compute targets. It is backed by Azure blob storage account.\n",
         "\n",
-        "Upload the MNIST files into a directory named `mnist` at the root of the datastore: See [access data from your datastores](https://docs.microsoft.com/azure/machine-learning/service/how-to-access-data) for more information."
+        "The MNIST files are uploaded into a directory named `mnist` at the root of the datastore. See [access data from your datastores](https://docs.microsoft.com/bs-latn-ba/azure/machine-learning/service/how-to-access-data) for more information."
       ]
     },
     {
@@ -690,4 +690,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 2
-}
+}

tutorials/regression-automated-ml.yml

@@ -4,4 +4,4 @@ dependencies:
   - azureml-sdk
   - azureml-train-automl
   - azureml-widgets
-  - azureml-opendatasets
+  - azureml-opendatasets