jprdonnelly/MachineLearningNotebooks
1
0
Fork 0
mirror of https://github.com/Azure/MachineLearningNotebooks.git synced 2025-12-20 09:37:04 -05:00
Code Issues Projects Releases Wiki Activity

Fixed links to configuration notebook.

Browse Source
This commit is contained in:
Jeff Shepherd
2019-02-15 11:31:10 -08:00
parent 094b4b3b13
commit ddd025e83e
1 changed files with 5 additions and 5 deletions
Download Patch File Download Diff File Expand all files Collapse all files

10
how-to-use-azureml/automated-machine-learning/README.md
View File

@@ -25,7 +25,7 @@ Below are the three execution environments supported by AutoML.
1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks) 1. [![Azure Notebooks](https://notebooks.azure.com/launch.png)](https://aka.ms/aml-clone-azure-notebooks)
[Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks. [Import sample notebooks ](https://aka.ms/aml-clone-azure-notebooks) into Azure Notebooks.
1. Follow the instructions in the [configuration](configuration.ipynb) notebook to create and connect to a workspace. 1. Follow the instructions in the [configuration](../../configuration.ipynb) notebook to create and connect to a workspace.
1. Open one of the sample notebooks. 1. Open one of the sample notebooks.
<a name="databricks"></a> <a name="databricks"></a>
@@ -90,7 +90,7 @@ bash automl_setup_linux.sh
``` ```
### 4. Running configuration.ipynb ### 4. Running configuration.ipynb
- Before running any samples you next need to run the configuration notebook. Click on configuration.ipynb notebook - Before running any samples you next need to run the configuration notebook. Click on [configuration](../../configuration.ipynb) notebook
- Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*) - Execute the cells in the notebook to Register Machine Learning Services Resource Provider and create a workspace. (*instructions in notebook*)
### 5. Running Samples ### 5. Running Samples
@@ -99,9 +99,6 @@ bash automl_setup_linux.sh
<a name="samples"></a> <a name="samples"></a>
# Automated ML SDK Sample Notebooks # Automated ML SDK Sample Notebooks
- [configuration.ipynb](configuration.ipynb)
- Create new Azure ML Workspace
- Save Workspace configuration file
- [auto-ml-classification.ipynb](classification/auto-ml-classification.ipynb) - [auto-ml-classification.ipynb](classification/auto-ml-classification.ipynb)
- Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits) - Dataset: scikit learn's [digit dataset](http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_digits.html#sklearn.datasets.load_digits)
@@ -169,6 +166,9 @@ bash automl_setup_linux.sh
- How to specifying sample_weight - How to specifying sample_weight
- The difference that it makes to test results - The difference that it makes to test results
- [auto-ml-subsampling-local.ipynb](subsampling/auto-ml-subsampling-local.ipynb)
- How to enable subsampling
- [auto-ml-dataprep.ipynb](dataprep/auto-ml-dataprep.ipynb) - [auto-ml-dataprep.ipynb](dataprep/auto-ml-dataprep.ipynb)
- Using DataPrep for reading data - Using DataPrep for reading data