From 75448400799a5eadd45c2d77b87abf783841f4df Mon Sep 17 00:00:00 2001
From: Jen Looper <jen.looper@gmail.com>
Date: Thu, 7 Apr 2022 16:23:01 -0400
Subject: [PATCH] ensuring that all instrumented links point to the item, not
the moment
---
2-Symbolic/README.md | 2 +-
4-ComputerVision/08-TransferLearning/README.md | 2 +-
.../TransferLearningTF.ipynb | 2 +-
README.md | 18 +++++++++---------
4 files changed, 12 insertions(+), 12 deletions(-)
diff --git a/2-Symbolic/README.md b/2-Symbolic/README.md
index 4fa6466..01251f0 100644
--- a/2-Symbolic/README.md
+++ b/2-Symbolic/README.md
@@ -202,7 +202,7 @@ See [FamilyOntology.ipynb](FamilyOntology.ipynb) for an example of using Semanti
In most of the cases, ontologies are carefully created by hand. However, it is also possible to **mine** ontologies from unstructured data, for example, from natural language texts.
-One such attempt was done by Microsoft Research, and resulted in [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-33554-dmitryso).
+One such attempt was done by Microsoft Research, and resulted in [Microsoft Concept Graph](https://blogs.microsoft.com/ai/microsoft-researchers-release-graph-that-helps-machines-conceptualize/?WT.mc_id=academic-57639-dmitryso).
It is a large collection of entities grouped together using `is-a` inheritance relationship. It allows answering questions like "What is Microsoft?" - the answer being something like "a company with probability 0.87, and a brand with probability 0.75".
diff --git a/4-ComputerVision/08-TransferLearning/README.md b/4-ComputerVision/08-TransferLearning/README.md
index bd9ab18..3f88460 100644
--- a/4-ComputerVision/08-TransferLearning/README.md
+++ b/4-ComputerVision/08-TransferLearning/README.md
@@ -20,7 +20,7 @@ Here are sample features extracted from a picture of a cat by VGG-16 network:
## Cats vs. Dogs Dataset
-In this example, we will use a dataset of [Cats and Dogs](https://www.microsoft.com/en-us/download/details.aspx?id=54765&WT.mc_id=academic-33554-dmitryso), which is very close to a real-life image classification scenario.
+In this example, we will use a dataset of [Cats and Dogs](https://www.microsoft.com/en-us/download/details.aspx?id=54765&WT.mc_id=academic-57639-dmitryso), which is very close to a real-life image classification scenario.
## Continue in Notebook
diff --git a/4-ComputerVision/08-TransferLearning/TransferLearningTF.ipynb b/4-ComputerVision/08-TransferLearning/TransferLearningTF.ipynb
index de86490..53e6bee 100644
--- a/4-ComputerVision/08-TransferLearning/TransferLearningTF.ipynb
+++ b/4-ComputerVision/08-TransferLearning/TransferLearningTF.ipynb
@@ -31,7 +31,7 @@
"source": [
"## Cats vs. Dogs Dataset\n",
"\n",
- "In this unit, we will solve a real-life problem of classifying images of cats and dogs. For this reason, we will use [Kaggle Cats vs. Dogs Dataset](https://www.kaggle.com/c/dogs-vs-cats), which can also be downloaded [from Microsoft](https://www.microsoft.com/en-us/download/details.aspx?id=54765&WT.mc_id=academic-33554-dmitryso).\n",
+ "In this unit, we will solve a real-life problem of classifying images of cats and dogs. For this reason, we will use [Kaggle Cats vs. Dogs Dataset](https://www.kaggle.com/c/dogs-vs-cats), which can also be downloaded [from Microsoft](https://www.microsoft.com/en-us/download/details.aspx?id=54765&WT.mc_id=academic-57639-dmitryso).\n",
"\n",
"Let's download this dataset and extract it into `data` directory (this process may take some time!):"
]
diff --git a/README.md b/README.md
index ad07eb6..c4ad3d5 100644
--- a/README.md
+++ b/README.md
@@ -24,14 +24,14 @@ In this curriculum, you will learn:
What we will not cover in this curriculum:
-* Business cases of using **AI in Business**. Consider taking [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-33554-dmitryso) learning path on Microsoft Learn, or [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-33554-dmitryso), developed in cooperation with [INSEAD](https://www.insead.edu/).
+* Business cases of using **AI in Business**. Consider taking [Introduction to AI for business users](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-57639-dmitryso) learning path on Microsoft Learn, or [AI Business School](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-57639-dmitryso), developed in cooperation with [INSEAD](https://www.insead.edu/).
* **Classic Machine Learning**, which is well described in our [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners)
-* Practical AI applications built using **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-33554-dmitryso)**. For this, we recommend that you start with modules Microsoft Learn for [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-33554-dmitryso), [natural language processing](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-33554-dmitryso) and others.
-* Specific ML **Cloud Frameworks**, such as [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-33554-dmitryso) or [Azure Databricks](). Consider using [Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-33554-dmitryso) and [Build and O perate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-33554-dmitryso) learning paths.
-* **Conversational AI** and **Chat Bots**. There is a separate [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-33554-dmitryso) learning path, and you can also refer to [this blog post](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) for more detail.
+* Practical AI applications built using **[Cognitive Services](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-57639-dmitryso)**. For this, we recommend that you start with modules Microsoft Learn for [vision](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-57639-dmitryso), [natural language processing](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-57639-dmitryso) and others.
+* Specific ML **Cloud Frameworks**, such as [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-57639-dmitryso) or [Azure Databricks](). Consider using [Build and operate machine learning solutions with Azure Machine Learning](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-57639-dmitryso) and [Build and O perate Machine Learning Solutions with Azure Databricks](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-57639-dmitryso) learning paths.
+* **Conversational AI** and **Chat Bots**. There is a separate [Create conversational AI solutions](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-57639-dmitryso) learning path, and you can also refer to [this blog post](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/) for more detail.
* **Deep Mathematics** behind deep learning. For this, we would recommend [Deep Learning](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618) by Ian Goodfellow, Yoshua Bengio and Aaron Courville, which is also available online at [https://www.deeplearningbook.org/](https://www.deeplearningbook.org/).
-For a gentle introduction to *AI in the Cloud* topic you may consider taking the [Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-33554-dmitryso) Learning Path.
+For a gentle introduction to *AI in the Cloud* topic you may consider taking the [Get started with artificial intelligence on Azure](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-57639-dmitryso) Learning Path.
---
# Content
@@ -56,8 +56,8 @@ For a gentle introduction to *AI in the Cloud* topic you may consider taking the
<td><a href="3-NeuralNetworks/05-Frameworks/IntroKerasTF.md">Keras/TensorFlow</td>
<td><a href="3-NeuralNetworks/05-Frameworks/lab/README.md">Lab</a></td></tr>
<tr><td>IV</td><td colspan="2"><b><a href="4-ComputerVision/README.md">Computer Vision</a></b></td>
- <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-33554-dmitryso">MS Learn</a></td>
- <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-33554-dmitryso">MS Learn</a></td>
+ <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-57639-dmitryso">MS Learn</a></td>
+ <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-57639-dmitryso">MS Learn</a></td>
<td>PAT</td></tr>
<tr><td>6</td><td>Intro to Computer Vision. OpenCV</td><td>Text<td colspan="2">Notebook</td><td></td></tr>
<tr><td>7</td><td>Convolutional Neural Networks<br/>CNN Architectures</td><td><a href="4-ComputerVision/07-ConvNets/README.md">Text</a><br/><a href="4-ComputerVision/07-ConvNets/CNN_Architectures.md">Text</a></td><td><a href="4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb">PyTorch</a></td><td><a href="4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb">TensorFlow</a></td><td><a href="4-ComputerVision/07-ConvNets/lab/README.md">Lab</a></td></tr>
@@ -67,8 +67,8 @@ For a gentle introduction to *AI in the Cloud* topic you may consider taking the
<tr><td>11</td><td>Object Detection</td><td>Text</td><td>PyTorch</td><td>TensorFlow</td><td></td></tr>
<tr><td>12</td><td>Instance Segmentation. U-Net</td><td>Text</td><td>PyTorch</td><td>TensorFlow</td><td></td></tr>
<tr><td>V</td><td colspan="2"><b><a href="5-NLP/README.md">Natural Language Processing</a></b></td>
- <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-33554-dmitryso">MS Learn</a></td>
- <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-33554-dmitryso">MS Learn</a></td>
+ <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-57639-dmitryso">MS Learn</a></td>
+ <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-57639-dmitryso">MS Learn</a></td>
<td>PAT</td></tr>
<tr><td>13</td><td>Text Representation. Bow/TF-IDF</td><td><a href="5-NLP/13-TextRep/README.md">Text</a></td><td><a href="5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb">PyTorch</a></td><td><a href="5-NLP/13-TextRep/TextRepresentationTF.ipynb">TensorFlow</td><td></td></tr>
<tr><td>14</td><td>Semantic word embeddings. Word2Vec and GloVe</td><td><a href="5-NLP/14-Embeddings/README.md">Text</td><td><a href="5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb">PyTorch</a></td><td><a href="5-NLP/14-Embeddings/EmbeddingsTF.ipynb">TensorFlow</a></td><td></td></tr>
--
GitLab