From 75448400799a5eadd45c2d77b87abf783841f4df Mon Sep 17 00:00:00 2001 From: Jen Looper 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 4fa64669..01251f03 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 bd9ab18f..3f884604 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 de864904..53e6bee7 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 ad07eb6b..c4ad3d5a 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 Keras/TensorFlow Lab IVComputer Vision - MS Learn - MS Learn + MS Learn + MS Learn PAT 6Intro to Computer Vision. OpenCVTextNotebook 7Convolutional Neural Networks
CNN ArchitecturesText
TextPyTorchTensorFlowLab @@ -67,8 +67,8 @@ For a gentle introduction to *AI in the Cloud* topic you may consider taking the 11Object DetectionTextPyTorchTensorFlow 12Instance Segmentation. U-NetTextPyTorchTensorFlow VNatural Language Processing - MS Learn - MS Learn + MS Learn + MS Learn PAT 13Text Representation. Bow/TF-IDFTextPyTorchTensorFlow 14Semantic word embeddings. Word2Vec and GloVeTextPyTorchTensorFlow