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

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",