From 5edbf6c76320cffefdcca51600b588e8a403175f Mon Sep 17 00:00:00 2001 From: Dmitri Soshnikov Date: Mon, 13 Sep 2021 17:58:32 +0300 Subject: [PATCH] Reformat main readme table --- README.md | 86 ++++++++++++++++++++++++++++++------------------------- 1 file changed, 47 insertions(+), 39 deletions(-) diff --git a/README.md b/README.md index 504e2776..002533ff 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about **Artificial Intelligence**. In this curriculum, you will learn: * Different approaches to Artificial Intelligence, including "good old" symbolic approach with **Knowledge Representation** and reasoning. -* **Neural Networks** and **Deep Learning**, which are at the core of modern AI. We will try to illustrate all ideas using code in two most popular frameworks - [Tensorflow](http://tensorflow.org) and [PyTorch](http://pytorch.org). +* **Neural Networks** and **Deep Learning**, which are at the core of modern AI. We will try to illustrate all ideas using code in two most popular frameworks - Tensorflow(http://tensorflow.org) and PyTorch(http://pytorch.org). * **Neural Architectures** for working with Images and Text. We will try to cover very recent models, but may lack a little bit on state-of-the-art. * Less popular AI approaches, such as **Genetic Algorithms** @@ -28,39 +28,52 @@ For a gentle introduction to *AI in the Cloud* topic you may consider taking [Ge --- # Content -| No | Lesson | Intro | PyTorch | Tensorflow | Lab | -|----|--------|-------|---------|------------|-----| -| **Introduction to AI** | [PAT] | -| 1 | Introduction and History of AI | [Text] | | | | -| **Symbolic AI** | [PAT] | -| 2 | Knowledge Representation and Expert Systems | [Text] | | | | -| **Introduction to Neural Networks** | [PAT] | -| 3 | Perceptron | [Text] | [Notebook] | -| 4 | Intro to Frameworks (PyTorch/Tensorflow) | [Text] | [PyTorch] | [Tensorflow] | | -| 5 | Multi-Layered Perceptron | [Text] | [PyTorch] | [Tensorflow] | | -| **Computer Vision** | [MS Learn][PTLearnCV] | [MS Learn][TFLearnCV] | [PAT] | -| 6 | Intro to Computer Vision. OpenCV | [Text] | [Notebook] | | -| 7 | Convolutional Neural Networks | [Text] | [PyTorch] | [Tensorflow] | | -| 8 | Pre-trained Networks and Transfer Learning | [Text] | [PyTorch] | [Tensorflow] | | -| 9 | Autoencoders and VAEs | [Text] | [PyTorch] | [Tensorflow] | | -| 10 | Generative Adversarial Networks | [Text] | [PyTorch] | [Tensorflow] | | -| 11 | Object Detection | [Text] | [PyTorch] | [Tensorflow] | | -| 12 | Instance Segmentation. U-Net | [Text] | [PyTorch] | [Tensorflow] | | -| **Natural Language Processing** | [MS Learn][PTLearnNLP] | [MS Learn][TFLearnNLP] | [PAT] | -| 13 | Text Representation. Bow/TF-IDF | [Text] | [PyTorch] | [Tensorflow] | | -| 14 | Semantic Word Embeddings | [Text] | [PyTorch] | [Tensorflow] | | -| 15 | Training your own embeddings | [Text] | [PyTorch] | [Tensorflow] | | -| 16 | Recurrent Neural Networks| [Text] | [PyTorch] | [Tensorflow] | | -| 17 | Generative Recurrent Networks | [Text] | [PyTorch] | [Tensorflow] | | -| 18 | Language Modelling. BERT. Transformers. | [Text] | [PyTorch] | [Tensorflow] | | -| 19 | Named Entity Recognition. | [Text] | [PyTorch] | [Tensorflow] | | -| 20 | Text Generation using GPT | [Text] | [PyTorch] | [Tensorflow] | | -| **Other AI Techniques** | [PAT](1-intro/PAT.md) | -| 21 | Genetic Algorithms | [Text] | [Notebook] | -| 22 | Deep Reinforcement Learning | [Text] | [PyTorch] | [Tensorflow] | | -| 23 | Multi-Agent Systems | [Text] | | | | -| **AI Ethics** | [PAT] | -| 24 | AI Ethics and Responsible AI | [Text] | | | | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NoLessonIntroPyTorchTensorflowLab
IIntroduction to AIPAT
1Introduction and History of AIText
IISymbolic AIPAT
2 Knowledge Representation and Expert SystemsText
IIIIntroduction to Neural NetworksPAT
3PerceptronText + Perceptron
+ Own Framework
4 Intro to Frameworks (PyTorch/Tensorflow)TextPyTorchTensorflow
5 Multi-Layered PerceptronTextPyTorchTensorflow
IVComputer VisionMS LearnMS LearnPAT
6Intro to Computer Vision. OpenCVTextNotebook
7Convolutional Neural NetworksTextPyTorchTensorflow
8Pre-trained Networks and Transfer LearningTextPyTorchTensorflow
9Autoencoders and VAEsTextPyTorchTensorflow
10 Generative Adversarial NetworksTextPyTorchTensorflow
11Object DetectionTextPyTorchTensorflow
12Instance Segmentation. U-NetTextPyTorchTensorflow
VNatural Language ProcessingMS LearnMS LearnPAT
13Text Representation. Bow/TF-IDFTextPyTorchTensorflow
14Semantic Word EmbeddingsTextPyTorchTensorflow
15Training your own embeddingsTextPyTorchTensorflow
16Recurrent Neural NetworksTextPyTorchTensorflow
17Generative Recurrent NetworksTextPyTorchTensorflow
18Language Modelling. BERT. Transformers.TextPyTorchTensorflow
19Named Entity Recognition.TextPyTorchTensorflow
20Text Generation using GPTTextPyTorchTensorflow
VIOther AI TechniquesPAT
21Genetic AlgorithmsTextNotebook
22Deep Reinforcement LearningTextPyTorchTensorflow
23Multi-Agent SystemsText
VIIAI EthicsPAT
24AI Ethics and Responsible AIText
Each lesson contains some pre-reading material (linked as **Text** above), and some executable Jupyter Notebooks, which are often specific to the framework (**PyTorch** or **Tensorflow**). The executable notebook also contains a lot of theoretical material, so to understand the topic you need to go through at least one version of the notebooks (either PyTorch or Tensorflow). There are also **Labs** available for some topics, which give you an opportunity to try applying the material you have learnt to some specific problem. @@ -132,8 +145,3 @@ Our team produces other curricula! Check out: - [IoT for Beginners](https://aka.ms/iot-beginners) - [Machine Learning for Beginners](http://github.com/microsoft/ML-for-Beginners) - [Data Science for Beginners](http://github.com/microsoft/Data-Science-for-Beginners) - -[TFLearnCV]: https://docs.microsoft.com/learn/modules/intro-computer-vision-tensorflow/?WT.mc_id=academic-33554-dmitryso -[TFLearnNLP]: https://docs.microsoft.com/learn/modules/intro-natural-language-processing-tensorflow/?WT.mc_id=academic-33554-dmitryso -[PTLearnCV]: https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-33554-dmitryso -[PTLearnNLP]: https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-33554-dmitryso \ No newline at end of file