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12
13# Artificial Intelligence for Beginners - A Curriculum
14
15|![ Sketchnote by [(@girlie_mac)](https://twitter.com/girlie_mac) ](./lessons/sketchnotes/ai-overview.png)|
16|:---:|
17| AI For Beginners - _Sketchnote by [@girlie_mac](https://twitter.com/girlie_mac)_ |
18
19Azure Cloud Advocates at Microsoft are pleased to offer a 12-week, 24-lesson curriculum all about **Artificial Intelligence**.
20
21In this curriculum, you will learn:
22
23* Different approaches to Artificial Intelligence, including the "good old" symbolic approach with **Knowledge Representation** and reasoning ([GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)).
24* **Neural Networks** and **Deep Learning**, which are at the core of modern AI. We will illustrate the concepts behind these important topics using code in two of the most popular frameworks - [TensorFlow](http://Tensorflow.org) and [PyTorch](http://pytorch.org).
25* **Neural Architectures** for working with images and text. We will cover recent models but may lack a little bit on the state-of-the-art.
26* Less popular AI approaches, such as **Genetic Algorithms** and **Multi-Agent Systems**.
27
28What we will not cover in this curriculum:
29
30* 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/).
31* **Classic Machine Learning**, which is well described in our [Machine Learning for Beginners Curriculum](http://github.com/Microsoft/ML-for-Beginners)
32* 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.
33* 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](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-57639-dmitryso). 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 Operate 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.
34* **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.
35* **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/).
36
37For a gentle introduction to *AI in the Cloud* topics 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.
38
39---
40# Content
41
42<table>
43<tr><th>No</th><th>Lesson</th><th>Intro</th><th>PyTorch</th><th>Keras/TensorFlow</th><th>Lab</th></tr>
44
45<tr><td>I</td><td colspan="4"><b>Introduction to AI</b></td><td></td></tr>
46<tr><td>1</td><td>Introduction and History of AI</td><td><a href="lessons/1-Intro/README.md">Text</a></td><td></td><td></td><td></td></tr>
47
48<tr><td>II</td><td colspan="4"><b>Symbolic AI</b></td><td></td></tr>
49<tr><td>2 </td><td>Knowledge Representation and Expert Systems</td><td><a href="lessons/2-Symbolic/README.md">Text</a></td><td colspan="2"><a href="lessons/2-Symbolic/Animals.ipynb">Expert System</a>, <a href="lessons/2-Symbolic/FamilyOntology.ipynb">Ontology</a>, <a href="lessons/2-Symbolic/MSConceptGraph.ipynb">Concept Graph</a></td><td></td></tr>
50<tr><td>III</td><td colspan="4"><b><a href="lessons/3-NeuralNetworks/README.md">Introduction to Neural Networks</a></b></td><td></td></tr>
51<tr><td>3</td><td>Perceptron</td>
52 <td><a href="lessons/3-NeuralNetworks/03-Perceptron/README.md">Text</a>
53 <td colspan="2"><a href="lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb">Notebook</a></td><td><a href="lessons/3-NeuralNetworks/03-Perceptron/lab/README.md">Lab</a></td></tr>
54<tr><td>4 </td><td>Multi-Layered Perceptron and Creating our own Framework</td><td><a href="lessons/3-NeuralNetworks/04-OwnFramework/README.md">Text</a></td><td colspan="2"><a href="lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb">Notebook</a><td><a href="lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md">Lab</a></td></tr>
55<tr><td>5</td>
56 <td>Intro to Frameworks (PyTorch/TensorFlow)<br/>Overfitting</td>
57 <td><a href="lessons/3-NeuralNetworks/05-Frameworks/README.md">Text</a><br/><a href="lessons/3-NeuralNetworks/05-Frameworks/Overfitting.md">Text</a></td>
58 <td><a href="lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb">PyTorch</td>
59 <td><a href="lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.md">Keras/TensorFlow</td>
60 <td><a href="lessons/3-NeuralNetworks/05-Frameworks/lab/README.md">Lab</a></td></tr>
61<tr><td>IV</td><td><b><a href="lessons/4-ComputerVision/README.md">Computer Vision</a></b></td>
62 <td colspan="3"><a href="https://docs.microsoft.com/learn/paths/explore-computer-vision-microsoft-azure/?WT.mc_id=academic-57639-dmitryso"><i>AI Fundamentals: Explore Computer Vision</i></a></td>
63 <td></td></tr>
64<tr><td></td><td colspan="2"><i>Microsoft Learn Module on Computer Vision</i></td>
65 <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-57639-dmitryso"><i>PyTorch</i></a></td>
66 <td><a href="https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-57639-dmitryso"><i>TensorFlow</i></a></td>
67 <td></td></tr>
68<tr><td>6</td><td>Intro to Computer Vision. OpenCV</td><td><a href="lessons/4-ComputerVision/06-IntroCV/README.md">Text</a><td colspan="2"><a href="lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb">Notebook</a></td><td><a href="lessons/4-ComputerVision/06-IntroCV/lab/README.md">Lab</a></td></tr>
69<tr><td>7</td><td>Convolutional Neural Networks<br/>CNN Architectures</td><td><a href="lessons/4-ComputerVision/07-ConvNets/README.md">Text</a><br/><a href="lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md">Text</a></td><td><a href="lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb">PyTorch</a></td><td><a href="lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb">TensorFlow</a></td><td><a href="lessons/4-ComputerVision/07-ConvNets/lab/README.md">Lab</a></td></tr>
70<tr><td>8</td><td>Pre-trained Networks and Transfer Learning<br/>Training Tricks</td><td><a href="lessons/4-ComputerVision/08-TransferLearning/README.md">Text</a><br/><a href="lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md">Text</a></td><td><a href="lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb">PyTorch</a></td><td><a href="lessons/4-ComputerVision/08-TransferLearning/TransferLearningTF.ipynb">TensorFlow</a><br/><a href="lessons/4-ComputerVision/08-TransferLearning/Dropout.ipynb">Dropout sample</a></td><td><a href="lessons/4-ComputerVision/08-TransferLearning/lab/README.md">Lab</a></td></tr>
71<tr><td>9</td><td>Autoencoders and VAEs</td><td><a href="lessons/4-ComputerVision/09-Autoencoders/README.md">Text</a></td><td><a href="lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPytorch.ipynb">PyTorch</td><td><a href="lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb">TensorFlow</a></td><td></td></tr>
72<tr><td>10</td><td>Generative Adversarial Networks<br/>Artistic Style Transfer</td><td><a href="lessons/4-ComputerVision/10-GANs/README.md">Text</a></td><td><a href="lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb">PyTorch</td><td><a href="lessons/4-ComputerVision/10-GANs/GANTF.ipynb">TensorFlow GAN</a><br/><a href="lessons/4-ComputerVision/10-GANs/StyleTransfer.ipynb">Style Transfer</a></td><td></td></tr>
73<tr><td>11</td><td>Object Detection</td><td><a href="lessons/4-ComputerVision/11-ObjectDetection/README.md">Text</a></td><td>PyTorch</td><td><a href="lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection-TF.ipynb">TensorFlow</td><td><a href="lessons/4-ComputerVision/11-ObjectDetection/lab/README.md">Lab</a></td></tr>
74<tr><td>12</td><td>Semantic Segmentation. U-Net</td><td><a href="lessons/4-ComputerVision/12-Segmentation/README.md">Text</a></td><td><a href="lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb">PyTorch</td><td><a href="lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb">TensorFlow</td><td></td></tr>
75<tr><td>V</td><td><b><a href="lessons/5-NLP/README.md">Natural Language Processing</a></b></td>
76 <td colspan="3"><a href="https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-57639-dmitryso"><i>AI Fundamentals: Explore Natural Language Processing</i></a></td>
77 <td></td></tr>
78<tr><td></td><td colspan="2"><i>Microsoft Learn Module on Natural Language</i></td>
79 <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-57639-dmitryso"><i>PyTorch</i></a></td>
80 <td><a href="https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-57639-dmitryso"><i>TensorFlow</i></a></td>
81 <td></td></tr>
82<tr><td>13</td><td>Text Representation. Bow/TF-IDF</td><td><a href="lessons/5-NLP/13-TextRep/README.md">Text</a></td><td><a href="lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb">PyTorch</a></td><td><a href="lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb">TensorFlow</td><td></td></tr>
83<tr><td>14</td><td>Semantic word embeddings. Word2Vec and GloVe</td><td><a href="lessons/5-NLP/14-Embeddings/README.md">Text</td><td><a href="lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb">PyTorch</a></td><td><a href="lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb">TensorFlow</a></td><td></td></tr>
84<tr><td>15</td><td>Language Modeling. Training your own embeddings</td><td><a href="lessons/5-NLP/15-LanguageModeling/README.md">Text</a></td><td></td><td><a href="lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb">TensorFlow</a></td><td><a href="lessons/5-NLP/15-LanguageModeling/lab/README.md">Lab</a></td></tr>
85<tr><td>16</td><td>Recurrent Neural Networks</td><td><a href="lessons/5-NLP/16-RNN/README.md">Text</a></td><td><a href="lessons/5-NLP/16-RNN/RNNPyTorch.ipynb">PyTorch</a></td><td><a href="lessons/5-NLP/16-RNN/RNNTF.ipynb">TensorFlow</a></td><td></td></tr>
86<tr><td>17</td><td>Generative Recurrent Networks</td><td><a href="lessons/5-NLP/17-GenerativeNetworks/README.md">Text</a></td><td><a href="lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.md">PyTorch</a></td><td><a href="lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.md">TensorFlow</a></td><td><a href="lessons/5-NLP/17-GenerativeNetworks/lab/README.md">Lab</a></td></tr>
87<tr><td>18</td><td>Transformers. BERT.</td><td><a href="lessons/5-NLP/18-Transformers/README.md">Text</a></td><td><a href="lessons/5-NLP/18-Transformers/TransformersPyTorch.md">PyTorch</a></td><td><a href="lessons/5-NLP/18-Transformers/TransformersTF.md">TensorFlow</a></td><td></td></tr>
88<tr><td>19</td><td>Named Entity Recognition</td><td><a href="lessons/5-NLP/19-NER/README.md">Text</a></td><td></td><td><a href="lessons/5-NLP/19-NER/NER-TF.ipynb">TensorFlow</a></td><td><a href="lessons/5-NLP/19-NER/lab/README.md">Lab</a></td></tr>
89<tr><td>20</td><td>Large Language Models, Prompt Programming and Few-Shot Tasks</td><td><a href="lessons/5-NLP/20-LangModels/README.md">Text</a></td><td><a href="lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb">PyTorch</td><td></td><td></td></tr>
90<tr><td>VI</td><td colspan="4"><b>Other AI Techniques</b></td><td></td></tr>
91<tr><td>21</td><td>Genetic Algorithms</td><td><a href="lessons/6-Other/21-GeneticAlgorithms/README.md">Text</a><td colspan="2"><a href="lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb">Notebook</a></td><td></td></tr>
92<tr><td>22</td><td>Deep Reinforcement Learning</td><td><a href="lessons/6-Other/22-DeepRL/README.md">Text</a></td><td></td><td><a href="lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb">TensorFlow</td><td><a href="lessons/6-Other/22-DeepRL/lab/README.md">Lab</a></td></tr>
93<tr><td>23</td><td>Multi-Agent Systems</td><td><a href="lessons/6-Other/23-MultiagentSystems/README.md">Text</a></td><td></td><td></td><td></td></tr>
94<tr><td>VII</td><td colspan="4"><b>AI Ethics</b></td><td></td></tr>
95<tr><td>24</td><td>AI Ethics and Responsible AI</td><td><a href="lessons/7-Ethics/README.md">Text</a></td><td colspan="2"><a href="https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-57639-dmitryso"><i>MS Learn: Responsible AI Principles</i></a></td><td></td></tr>
96<tr><td></td><td colspan="4"><b>Extras</b></td><td></td></tr>
97<tr><td>X1</td><td>Multi-Modal Networks, CLIP and VQGAN</td><td><a href="lessons/X-Extras/X1-MultiModal/README.md">Text</a></td><td colspan="2"><a href="lessons/X-Extras/X1-MultiModal/Clip.ipynb">Notebook</a></td><td></td></tr>
98</table>
99
100**[Mindmap of the Course](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)**
101
102Each 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 learned to a specific problem.
103
104Some sections also contain links to **MS Learn** modules that cover related topics. Microsoft Learn provides a convenient GPU-enabled learning environment, although in terms of content you can expect this curriculum to go a bit deeper.
105
106# Getting Started
107
108**Students**, there are a couple of ways to use the curriculum. First of all, you can just read the text and look through the code directly on GitHub. If you want to run the code in any of the notebooks - [read our instructions](./etc/how-to-run.md), and find more advice on how to do it [in this blog post](https://soshnikov.com/education/how-to-execute-notebooks-from-github/).
109
110> **Note**: [Instructions on how to run the code in this curriculum](/etc/how-to-run.md)
111
112However, if you would like to take the course as a self-study project, we suggest that you fork the entire repo to your own GitHub account and complete the exercises on your own or with a group:
113
114- Start with a pre-lecture quiz
115- Read the intro text for the lecture
116- If the lecture has additional notebooks, go through them, reading and executing the code. If both TensorFlow and PyTorch notebooks are provided, you can focus on one of them - chose your favorite framework
117- Notebooks often contain some of the challenges that require you to tweak the code a little bit to experiment
118- Take the post-lecture quiz
119- If there is a lab attached to the module - complete the assignment
120- Visit the [Discussion board](https://github.com/microsoft/AI-For-Beginners/discussions) to "learn out loud".
121- Chat with other learners [on Gitter](https://gitter.im/Microsoft/ai-for-beginners)
122
123> For further study, we recommend following these [Microsoft Learn](https://docs.microsoft.com/users/jenlooper-2911/collections/k7o7tg1gp306q4?WT.mc_id=academic-15963-cxa) modules and learning paths.
124
125**Teachers**, we have [included some suggestions](/etc/for-teachers.md) on how to use this curriculum.
126
127---
128
129## Credits
130
131**✍️ Primary Author:** [Dmitry Soshnikov](http://soshnikov.com), PhD <br/>
132**🔥 Editor:** [Jen Looper](https://twitter.com/jenlooper), PhD <br/>
133**🎨 Sketchnote illustrator:** [Tomomi Imura](https://twitter.com/girlie_mac) <br/>
134**✅ Quiz Creator:** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/) <br/>
135**🙏 Core Contributors:** [Evgenii Pishchik](https://github.com/Pe4enIks)
136
137## Meet the Team
138
139[![Promo video](/lessons/sketchnotes/ai-for-beginners.png)](https://youtu.be/m2KrAk0cC1c "Promo video")
140
141> 🎥 Click the image above for a video about the project and the folks who created it!
142
143---
144
145## Pedagogy
146
147We have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on **project-based** and that it includes **frequent quizzes**.
148
149By ensuring that the content aligns with projects, the process is made more engaging for students and retention of concepts will be augmented. In addition, a low-stakes quiz before a class sets the intention of the student towards learning a topic, while a second quiz after class ensures further retention. This curriculum was designed to be flexible and fun and can be taken in whole or in part. The projects start small and become increasingly complex by the end of the 12 week cycle.
150
151> Find our [Code of Conduct](etc/CODE_OF_CONDUCT.md), [Contributing](etc/CONTRIBUTING.md), and [Translation](etc/TRANSLATIONS.md) guidelines. Find our [Support Documentation here](etc/SUPPORT.md) and [security information here](etc/SECURITY.md). We welcome your constructive feedback!
152
153> **A note about quizzes**: All quizzes are contained [in this app](https://black-ground-0cc93280f.1.azurestaticapps.net/), for 50 total quizzes of three questions each. They are linked from within the lessons but the quiz app can be run locally; follow the instruction in the `etc/quiz-app` folder.
154
155## Offline access
156
157You can run this documentation offline by using [Docsify](https://docsify.js.org/#/). Fork this repo, [install Docsify](https://docsify.js.org/#/quickstart) on your local machine, and then in the `etc/docsify` folder of this repo, type `docsify serve`. The website will be served on port 3000 on your localhost: `localhost:3000`. A pdf of the curriculum is available [at this link](/etc/pdf/readme.pdf).
158
159## Help Wanted!
160
161Would you like to contribute a translation? Please read our [translation guidelines](etc/TRANSLATIONS.md).
162
163## Other Curricula
164
165Our team produces other curricula! Check out:
166
167- [Web Dev for Beginners](https://aka.ms/webdev-beginners)
168- [IoT for Beginners](https://aka.ms/iot-beginners)
169- [Machine Learning for Beginners](http://aka.ms/ML-for-Beginners)
170- [Data Science for Beginners](http://aka.ms/Data-Science-for-Beginners)
171