AI-For-Beginners - 12 Weeks, 24 Lessons, AI for All!

Created at: 2021-03-04 00:27:36
Language: Jupyter Notebook
License: MIT

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Artificial Intelligence for Beginners - A Curriculum

 Sketchnote by (@girlie_mac)
AI For Beginners - Sketchnote by @girlie_mac

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 the "good old" symbolic approach with Knowledge Representation and reasoning (GOFAI).
  • 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 and PyTorch.
  • 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.
  • Less popular AI approaches, such as Genetic Algorithms and Multi-Agent Systems.

What we will not cover in this curriculum:

For a gentle introduction to AI in the Cloud topics you may consider taking the Get started with artificial intelligence on Azure Learning Path.


Content

No Lesson Intro PyTorch Keras/TensorFlow Lab
I Introduction to AI
1 Introduction and History of AI Text
II Symbolic AI
2 Knowledge Representation and Expert Systems Text Expert System, Ontology, Concept Graph
III Introduction to Neural Networks
3 Perceptron Text Notebook Lab
4 Multi-Layered Perceptron and Creating our own Framework Text Notebook Lab
5 Intro to Frameworks (PyTorch/TensorFlow)
Overfitting
Text
Text
PyTorch Keras/TensorFlow Lab
IV Computer Vision AI Fundamentals: Explore Computer Vision
Microsoft Learn Module on Computer Vision PyTorch TensorFlow
6 Intro to Computer Vision. OpenCV Text Notebook Lab
7 Convolutional Neural Networks
CNN Architectures
Text
Text
PyTorch TensorFlow Lab
8 Pre-trained Networks and Transfer Learning
Training Tricks
Text
Text
PyTorch TensorFlow
Dropout sample
Adversarial Cat
Lab
9 Autoencoders and VAEs Text PyTorch TensorFlow
10 Generative Adversarial Networks
Artistic Style Transfer
Text PyTorch TensorFlow GAN
Style Transfer
11 Object Detection Text PyTorch TensorFlow Lab
12 Semantic Segmentation. U-Net Text PyTorch TensorFlow
V Natural Language Processing AI Fundamentals: Explore Natural Language Processing
Microsoft Learn Module on Natural Language PyTorch TensorFlow
13 Text Representation. Bow/TF-IDF Text PyTorch TensorFlow
14 Semantic word embeddings. Word2Vec and GloVe Text PyTorch TensorFlow
15 Language Modeling. Training your own embeddings Text TensorFlow Lab
16 Recurrent Neural Networks Text PyTorch TensorFlow
17 Generative Recurrent Networks Text PyTorch TensorFlow Lab
18 Transformers. BERT. Text PyTorch TensorFlow
19 Named Entity Recognition Text TensorFlow Lab
20 Large Language Models, Prompt Programming and Few-Shot Tasks Text PyTorch
VI Other AI Techniques
21 Genetic Algorithms Text Notebook
22 Deep Reinforcement Learning Text TensorFlow Lab
23 Multi-Agent Systems Text
VII AI Ethics
24 AI Ethics and Responsible AI Text MS Learn: Responsible AI Principles
Extras
X1 Multi-Modal Networks, CLIP and VQGAN Text Notebook

Mindmap of the Course

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 learned to a specific problem.

Some 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.

Getting Started

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, and find more advice on how to do it in this blog post.

Note: Instructions on how to run the code in this curriculum

However, 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:

  • Start with a pre-lecture quiz
  • Read the intro text for the lecture
  • 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
  • Notebooks often contain some of the challenges that require you to tweak the code a little bit to experiment
  • Take the post-lecture quiz
  • If there is a lab attached to the module - complete the assignment
  • Visit the Discussion board to "learn out loud".
  • Chat with other learners on Gitter or in Telegram channel.

For further study, we recommend following these Microsoft Learn modules and learning paths.

Teachers, we have included some suggestions on how to use this curriculum.


Credits

✍️ Primary Author: Dmitry Soshnikov, PhD
🔥 Editor: Jen Looper, PhD
🎨 Sketchnote illustrator: Tomomi Imura
Quiz Creator: Lateefah Bello, MLSA
🙏 Core Contributors: Evgenii Pishchik

Meet the Team

Promo video

🎥 Click the image above for a video about the project and the folks who created it!


Pedagogy

We have chosen two pedagogical tenets while building this curriculum: ensuring that it is hands-on project-based and that it includes frequent quizzes.

By 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.

Find our Code of Conduct, Contributing, and Translation guidelines. Find our Support Documentation here and security information here. We welcome your constructive feedback!

A note about quizzes: All quizzes are contained in this app, 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.

Offline access

You can run this documentation offline by using Docsify. Fork this repo, install Docsify 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.

Help Wanted!

Would you like to contribute a translation? Please read our translation guidelines.

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