jprdonnelly/MachineLearningNotebooks
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

update samples from Release-126 as a part of 1.39.0 SDK stable release

Browse Source
This commit is contained in:
amlrelsa-ms
2022-02-28 16:51:54 +00:00
parent 9094da4085
commit 84af0c482f
58 changed files with 2923 additions and 474 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
tutorials/compute-instance-quickstarts/quickstart-azureml-in-10mins/quickstart-azureml-in-10mins.ipynb
View File

@@ -19,7 +19,7 @@
"source": [
"# Quickstart: Train and deploy a model in Azure Machine Learning in 10 minutes\n",
"\n",
"In this quickstart, learn how to get started with Azure Machine Learning. You'll train an image classification model using the [MNIST](https://azure.microsoft.com/services/open-datasets/catalog/mnist/) dataset.\n",
"In this quickstart, learn how to get started with Azure Machine Learning. You'll train an image classification model using the [MNIST](https://docs.microsoft.com/azure/open-datasets/dataset-mnist) dataset.\n",
"\n",
"You'll learn how to:\n",
"\n",
@@ -280,7 +280,7 @@
"# get a curated environment\n",
"env = Environment.get(\n",
" workspace=ws, \n",
" name=\"AzureML-sklearn-0.24.1-ubuntu18.04-py37-cpu-inference\",\n",
" name=\"AzureML-sklearn-1.0-ubuntu20.04-py38-cpu\",\n",
" version=1\n",
")\n",
"env.inferencing_stack_version='latest'\n",

2
tutorials/compute-instance-quickstarts/quickstart-azureml-python-sdk/quickstart-azureml-python-sdk.ipynb
View File

@@ -21,7 +21,7 @@
"\n",
"In this quickstart, you learn how to submit a batch training job using the Python SDK. In this example, we submit the job to the 'local' machine (the compute instance you are running this notebook on). However, you can use exactly the same method to submit the job to different compute targets (for example, AKS, Azure Machine Learning Compute Cluster, Synapse, etc) by changing a single line of code. A full list of support compute targets can be viewed [here](https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target). \n",
"\n",
"This quickstart trains a simple logistic regression using the [MNIST](https://azure.microsoft.com/services/open-datasets/catalog/mnist/) dataset and [scikit-learn](http://scikit-learn.org) with Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing a number from 0 to 9. The goal is to create a multi-class classifier to identify the digit a given image represents. \n",
"This quickstart trains a simple logistic regression using the [MNIST](https://docs.microsoft.com/azure/open-datasets/dataset-mnist) dataset and [scikit-learn](http://scikit-learn.org) with Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing a number from 0 to 9. The goal is to create a multi-class classifier to identify the digit a given image represents. \n",
"\n",
"You will learn how to:\n",
"\n",

2
tutorials/image-classification-mnist-data/img-classification-part1-training.ipynb
View File

@@ -17,7 +17,7 @@
"\n",
"In this tutorial, you train a machine learning model on remote compute resources. You'll use the training and deployment workflow for Azure Machine Learning service (preview) in a Python Jupyter notebook. You can then use the notebook as a template to train your own machine learning model with your own data. This tutorial is **part one of a two-part tutorial series**. \n",
"\n",
"This tutorial trains a simple logistic regression using the [MNIST](https://azure.microsoft.com/services/open-datasets/catalog/mnist/) dataset and [scikit-learn](http://scikit-learn.org) with Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing a number from 0 to 9. The goal is to create a multi-class classifier to identify the digit a given image represents. \n",
"This tutorial trains a simple logistic regression using the [MNIST](https://docs.microsoft.com/azure/open-datasets/dataset-mnist) dataset and [scikit-learn](http://scikit-learn.org) with Azure Machine Learning. MNIST is a popular dataset consisting of 70,000 grayscale images. Each image is a handwritten digit of 28x28 pixels, representing a number from 0 to 9. The goal is to create a multi-class classifier to identify the digit a given image represents. \n",
"\n",
"Learn how to:\n",
"\n",