pull base content,head:wwlpublishsync,into:51002fee3029387e0935e2f5952572bbb4c2c80daafdbb00547f794808314b52-live

Author: wwlpublish

learn-pr/achievements/get-started-data-science-fabric-social.png

@@ -0,0 +1,34 @@
+### YamlMime:LearningPath
+uid: learn.wwl.introduction-microsoft-azure-management-tasks
+metadata:
+  title: Introduction to Microsoft Azure Management Tasks
+  description: This learning path helps you explore basic management tasks for Azure resources. You'll have the opportunity to learn about managing virtual machines, virtual networks, and storage accounts, and then a guided project will give you hands-on experience actually managing Azure resources.
+  ms.date: 07/04/2025
+  author: wwlpublish
+  ms.author: robbarefoot
+  ms.topic: learning-path
+title: Introduction to Microsoft Azure Management tasks
+prerequisites: |
+  -   Basic understanding of cloud computing.
+  -   Basic understanding of Microsoft Azure services such as compute, storage, and networking.
+  -   An active Microsoft Azure subscription.
+summary: |
+  This learning path helps you explore basic management tasks for Azure resources. You'll have the opportunity to learn about managing virtual machines, virtual networks, and storage accounts, and then a guided project will give you hands-on experience actually managing Azure resources.
+iconUrl: /training/achievements/generic-trophy.svg
+levels:
+- beginner
+roles:
+- business-user
+products:
+- azure
+subjects:
+- architecture
+- cloud-computing
+- infrastructure
+- networking
+- resource-management
+- virtual-machine
+modules:
+- learn.wwl.describe-microsoft-azure-resources-management
+trophy:
+  uid: learn.wwl.introduction-microsoft-azure-management-tasks.trophy

learn-pr/achievements/get-started-data-science-fabric.svg

@@ -4,9 +4,9 @@ title: Introduction
 metadata:
   title: Introduction
   description: Learn about Keras, a high-level API for the TensorFlow machine learning framework. 
-  author: bstollnitz
-  ms.author: bstollnitz
-  ms.date: 07/17/2023
+  author: robmcm
+  ms.author: robmcm
+  ms.date: 06/23/2025
   ms.topic: unit
   ms.product: learning-tensorflow
   ms.contributors:

learn-pr/paths/introduction-microsoft-azure-management-tasks/index.yml

@@ -4,9 +4,9 @@ title: Data
 metadata:
   title: Data
   description: Learn how to get your data ready for use by a neural network.
-  ms.date: 07/17/2023
-  author: bstollnitz
-  ms.author: bstollnitz
+  ms.date: 06/23/2025
+  author: robmcm
+  ms.author: robmcm
   ms.topic: unit
   ms.product: learning-tensorflow
   ms.contributors:
@@ -23,13 +23,13 @@ quiz:
     choices:
     - content: "We didn't need to. For a small amount of data like in this sample, we could just use NumPy arrays."
       isCorrect: false
-      explanation: "This is true, but other options are also true."
+      explanation: "This option is true, but other options are also true."
     - content: "It's a good idea to learn about it, especially if we're going to work with larger data in the future."
       isCorrect: false
-      explanation: "This is true, but other options are also true."
+      explanation: "This option is true, but other options are also true."
     - content: "Because it returns our data in batches of 64 images when we iterate over it, which we use later when training."
       isCorrect: false
-      explanation: "This is true, but other options are also true."
-    - content: "All of the above."
+      explanation: "This option is true, but other options are also true."
+    - content: "All of these options are true."
       isCorrect: true
       explanation: "Correct!"

learn-pr/tensorflow/intro-machine-learning-keras/1-introduction.yml

@@ -4,9 +4,9 @@ title: Neural network architecture
 metadata:
   title: Neural network architecture
   description: Learn how to create a neural network using Keras and TensorFlow.
-  ms.date: 07/17/2023
-  author: bstollnitz
-  ms.author: bstollnitz
+  ms.date: 06/23/2025
+  author: robmcm
+  ms.author: robmcm
   ms.topic: unit
   ms.product: learning-tensorflow
   ms.contributors:

learn-pr/tensorflow/intro-machine-learning-keras/2-data.yml

@@ -4,9 +4,9 @@ title: Training and testing the neural network
 metadata:
   title: Training and testing the neural network
   description: Learn how to train your neural network using the training data, and how to evaluate it using the test data.
-  ms.date: 07/17/2023
-  author: bstollnitz
-  ms.author: bstollnitz
+  ms.date: 06/23/2025
+  author: robmcm
+  ms.author: robmcm
   ms.topic: unit
   ms.product: learning-tensorflow
   ms.contributors:

learn-pr/tensorflow/intro-machine-learning-keras/3-neural-network.yml

@@ -4,9 +4,9 @@ title: Making a prediction
 metadata:
   title: Making a prediction
   description: Learn how to make a prediction by using your trained neural network.
-  ms.date: 07/17/2023
-  author: bstollnitz
-  ms.author: bstollnitz
+  ms.date: 06/23/2025
+  author: robmcm
+  ms.author: robmcm
   ms.topic: unit
   ms.product: learning-tensorflow
   ms.contributors:
@@ -29,7 +29,7 @@ quiz:
       explanation: "Incorrect. Our model generalizes beyond the training data. In fact, it makes good predictions for test data."
     - content: "Only images in the training data or test data."
       isCorrect: false
-      explanation: "Incorrect. You saw it work for test data that the model hadn't seen during training. You can imagine that it works similarly for other images it hasn't seen."
+      explanation: "Incorrect. You saw it work for test data that the model didn't see during training. You can imagine that it works similarly for other images it didn't see."
     - content: "Any image, so long as it's in the format expected by the model."
       isCorrect: true
       explanation: "Correct! The more similar the prediction images are to the images in the training data, the better the predictions are."

learn-pr/tensorflow/intro-machine-learning-keras/4-train.yml

@@ -4,9 +4,9 @@ title: Summary
 metadata:
   title: Summary
   description: Review what you learned about Keras and TensorFlow machine learning models.
-  ms.date: 07/17/2023
-  author: bstollnitz
-  ms.author: bstollnitz
+  ms.date: 06/23/2025
+  author: robmcm
+  ms.author: robmcm
   ms.topic: unit
   ms.product: learning-tensorflow
   ms.contributors: bstollnitz-08202021

learn-pr/tensorflow/intro-machine-learning-keras/5-predict.yml

@@ -1,20 +1,22 @@
 
-TensorFlow is a popular framework for working with machine learning. This module covers Keras, which is a higher-level and user-friendly API that's released as part of TensorFlow. For many scenarios, the level of abstraction provided by Keras gives you all the functionality you need, without the complexity of the lower-level concepts of TensorFlow.
+TensorFlow is a popular framework for working with machine learning. This module covers Keras, which is a higher-level and user-friendly API released as part of TensorFlow. For many scenarios, the level of abstraction provided by Keras gives you all the functionality you need, without the complexity of the lower-level concepts of TensorFlow.
 
-At the end of this module, if you feel that you need more flexibility than Keras provides, then the fifth module of this learning path, *Intro to Machine Learning with TensorFlow*, shows how to reimplement a portion of the Keras code in this module using lower-level TensorFlow APIs.
+At the end of this module, if you feel that you need more flexibility than Keras provides, then the fifth module of this learning path, *Go beyond Keras: Customize with TensorFlow*, shows how to reimplement a portion of the Keras code in this module using lower-level TensorFlow APIs.
 
-Both this module and module five show how you can create a basic neural network using the Fashion MNIST dataset as a data source. You'll build a neural network that takes images of clothing as input, and then classifies them according to their contents, such as *Shirt*, *Coat*, or *Dress*.
+Both this module and module five show how you can create a basic neural network using the Fashion MNIST dataset as a data source. You're going to build a neural network that takes images of clothing as input, and then classifies them according to their contents, such as *Shirt*, *Coat*, or *Dress*.
 
 For this module, we assume that you're comfortable with Python, but we don't assume any knowledge of Keras or TensorFlow.
 
 Let's get started!
 
 ## Learning objectives
+
 - Learn to load and prepare data to be used in machine learning.
 - Learn to specify the architecture of a deep learning neural network.
 - Learn to train a neural network.
 - Learn to make a prediction by using a neural network.
 
 ## Prerequisites
+
 - Knowledge of Python
 - Basic knowledge about how to use Jupyter Notebooks