update link to image

Fix for Tensorflow 2.0 related Notebook Failures

README.md

@@ -13,7 +13,8 @@ Read more detailed instructions on [how to set up your environment](./NBSETUP.md
 
 ## How to navigate and use the example notebooks?
 
-Use this [index](https://github.com/Azure/MachineLearningNotebooks/blob/master/index.md) to assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content. 
+This [index](https://github.com/Azure/MachineLearningNotebooks/blob/master/index.md) should assist in navigating the Azure Machine Learning notebook samples and encourage efficient retrieval of topics and content. 
+
 
 If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, you should always run the [Configuration](./configuration.ipynb) notebook first when setting up a notebook library on a new machine or in a new environment. It configures your notebook library to connect to an Azure Machine Learning workspace, and sets up your workspace and compute to be used by many of the other examples. 
 

how-to-use-azureml/deployment/accelerated-models/accelerated-models-object-detection.yml

@@ -2,7 +2,6 @@ name: accelerated-models-object-detection
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-accel-models
+  - azureml-accel-models[cpu]
-  - tensorflow
   - opencv-python
   - matplotlib

how-to-use-azureml/deployment/accelerated-models/accelerated-models-quickstart.yml

@@ -2,5 +2,4 @@ name: accelerated-models-quickstart
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-accel-models
+  - azureml-accel-models[cpu]
-  - tensorflow

how-to-use-azureml/deployment/accelerated-models/accelerated-models-training.yml

@@ -2,8 +2,7 @@ name: accelerated-models-training
 dependencies:
 - pip:
   - azureml-sdk
-  - azureml-accel-models
+  - azureml-accel-models[cpu]
-  - tensorflow
   - keras
   - tqdm 
   - sklearn

how-to-use-azureml/explain-model/tabular-data/simple-feature-transformations-explain-local.ipynb

@@ -49,7 +49,7 @@
         "\n",
         "We will showcase raw feature transformations with three tabular data explainers: TabularExplainer (SHAP), MimicExplainer (global surrogate), and PFIExplainer.\n",
         "\n",
-        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.PNG) |\n",
+        "| ![Interpretability Toolkit Architecture](./img/interpretability-architecture.png) |\n",
         "|:--:|\n",
         "| *Interpretability Toolkit Architecture* |\n",
         "\n",

index.md

@@ -17,7 +17,7 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 
 |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
 |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
-| [Use a notebook to train](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb) | Training and deploying a model from a notebook | Diabetes | Local | Azure Container Instance | None | None |
+| [Using a notebook for training and deploying](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/training/train-within-notebook/train-within-notebook.ipynb) | Training and deploying a model from a notebook | Diabetes | Local | Azure Container Instance | None | None |
 | [Use MLflow with Azure Machine Learning for training and deployment](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/using-mlflow/train-deploy-pytorch/train-and-deploy-pytorch.ipynb) | Use MLflow with Azure Machine Learning to train and deploy Pa yTorch image classifier model | MNIST | AML Compute | Azure Container Instance | PyTorch | None |
 | :star:[Azure Machine Learning Pipeline with DataTranferStep](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-data-transfer.ipynb) | Demonstrates the use of DataTranferStep | Custom | ADF | None | Azure ML | None |
 | [Getting Started with Azure Machine Learning Pipelines](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-getting-started.ipynb) | Getting Started notebook for ANML Pipelines | Custom | AML Compute | None | Azure ML | None |
@@ -75,7 +75,7 @@ Machine Learning notebook samples and encourage efficient retrieval of topics an
 ## Other Notebooks
 |Title| Task | Dataset | Training Compute | Deployment Target | ML Framework | Tags |
 |:----|:-----|:-------:|:----------------:|:-----------------:|:------------:|:------------:|
-| [Logging APIs](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb) | Logging APIs and analyzing results | None | None | None | None | None |
+| [Logging APIs](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/track-and-monitor-experiments/logging-api/logging-api.ipynb) | Logging APIs and analyzing results | None | None | None | None | None |
 | [configuration](https://github.com/Azure/MachineLearningNotebooks/blob/master/configuration.ipynb) |  |  |  |  |  |  |
 | [azure-ml-with-nvidia-rapids](https://github.com/Azure/MachineLearningNotebooks/blob/master//contrib/RAPIDS/azure-ml-with-nvidia-rapids.ipynb) |  |  |  |  |  |  |
 | [auto-ml-classification](https://github.com/Azure/MachineLearningNotebooks/blob/master//how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb) |  |  |  |  |  |  |