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Artificial Intelligence for Beginners - A Curriculum

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

Explore the world of Artificial Intelligence (AI) with our 12-week, 24-lesson curriculum! It includes practical lessons, quizzes, and labs. The curriculum is beginner-friendly and covers tools like TensorFlow and PyTorch, as well as ethics in AI

What you will learn

Mindmap of the Course

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 be a bit lacking in 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:

Find all additional resources for this course in our Microsoft Learn collection

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

Lesson Link PyTorch/Keras/TensorFlow Lab
0 Course Setup Setup Your Development Environment
I Introduction to AI
01 Introduction and History of AI - -
II Symbolic AI
02 Knowledge Representation and Expert Systems Expert Systems / Ontology /Concept Graph
III Introduction to Neural Networks
03 Perceptron Notebook Lab
04 Multi-Layered Perceptron and Creating our own Framework Notebook Lab
05 Intro to Frameworks (PyTorch/TensorFlow) and Overfitting PyTorch / Keras / TensorFlow Lab
IV Computer Vision PyTorch / TensorFlow Explore Computer Vision on Microsoft Azure
06 Intro to Computer Vision. OpenCV Notebook Lab
07 Convolutional Neural Networks & CNN Architectures PyTorch /TensorFlow Lab
08 Pre-trained Networks and Transfer Learning and Training Tricks PyTorch / TensorFlow Lab
09 Autoencoders and VAEs PyTorch / TensorFlow
10 Generative Adversarial Networks & Artistic Style Transfer PyTorch / TensorFlow
11 Object Detection TensorFlow Lab
12 Semantic Segmentation. U-Net PyTorch / TensorFlow
V Natural Language Processing PyTorch /TensorFlow Explore Natural Language Processing on Microsoft Azure
13 Text Representation. Bow/TF-IDF PyTorch / TensorFlow
14 Semantic word embeddings. Word2Vec and GloVe PyTorch / TensorFlow
15 Language Modeling. Training your own embeddings PyTorch / TensorFlow Lab
16 Recurrent Neural Networks PyTorch / TensorFlow
17 Generative Recurrent Networks PyTorch / TensorFlow Lab
18 Transformers. BERT. PyTorch /TensorFlow
19 Named Entity Recognition TensorFlow Lab
20 Large Language Models, Prompt Programming and Few-Shot Tasks PyTorch
VI Other AI Techniques
21 Genetic Algorithms Notebook
22 Deep Reinforcement Learning PyTorch /TensorFlow Lab
23 Multi-Agent Systems
VII AI Ethics
24 AI Ethics and Responsible AI Microsoft Learn: Responsible AI Principles
IX Extras
25 Multi-Modal Networks, CLIP and VQGAN Notebook

Each lesson contains

  • Pre-reading material
  • 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 notebook (either PyTorch or TensorFlow).
  • Labs available for some topics, which give you an opportunity to try applying the material you have learned to a specific problem.
  • Some sections contain links to MS Learn modules that cover related topics.

Getting Started

We have created a setup lesson to help you with setting up your development environment. For Educators, we have created a curricula setup lesson for you too!

Don't forget to star (🌟) this repo to find it easier later.

Meet other Learners

Join our official AI Discord server to meet and network with other learners taking this course and get support.

Help Wanted

Do you have suggestions or found spelling or code errors? Raise an issue or create a pull request.

Special Thanks

Other Curricula

Our team produces other curricula! Check out:

ai-for-beginners's People

Contributors

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ai-for-beginners's Issues

QA Tasks for: 11 - Object Detection

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

QA Tasks for: 3 - Perceptron

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

QA Tasks for: 23 - Multi Agent Systems

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

QA Tasks for: 17 - Generative RN

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

favicon missing

Add the love icon to be consistent with the other for beginner content

QA Tasks for: 15 - Lang modeling

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

QA Tasks for: Intro

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

QA Tasks for: 13 - Text Representation

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

QA Tasks for: 14 - Semantic embeddings

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

QA Tasks for: 4 - Multi-Layered Perceptron

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

QA Tasks for: 5 - Intro to frameworks

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

QA Tasks for: 2 - Knowledge Systems

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

Neural Network isn't how children learns

"We can construct a so-called artificial neural network inside a computer, and then try to teach it to solve problems by giving it examples. This process is similar to how a newborn child learns about his or her surroundings by making observations."

Uh, no. Neural networks are inspired by how neurons in our brain works internally, with A LOT of connections between different neurons, the same as layers in a neural network. But it's not how children learns. How this works with neural networks is that you give it A LOT OF DATA with specified connections and it builds an internal representation getting closer and closer to the result you expect. Also known as a model.

Children doesn't learn like that. A child learns more with smaller steps (it's called "baby steps" for a reason), by filtering out a lot of noise and focusing on single things and build up understanding of the world that way. Just like any other human learns things really. You don't learn a language by spamming data, you learn it step by step. Good luck feeding whole internet to a child and see how that goes regarding learning about things.

QA Tasks for: 16 - Recurrent NN

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

QA Tasks for: 24 - AI Ethics

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

a little bit outdated?

Just read the GPT part and after this:

"GPT is a Family
GPT is not a single model, but rather a collection of models developed and trained by OpenAI. The latest model openly available is GPT-2, which has up to 1.5 billion parameters (there are several variations of the model, so you can select one for your tasks that is a good compromise between size/performance). Latest GPT-3 model has up to 175 billion parameters, and is available as a cognitive service from Microsoft Azure, and as OpenAI API."

I thought the course needs an update :).

Cheers, M.

QA Tasks for: 6 - Intro to Computer Vision

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

Link to quizzes are broken

When open quizzes of lesson 1, 2 and 3 the azurestaticapp return 404 message.
I assume the same problem is happening with other lessons as well.

QA Tasks for: 10 - GANs

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

QA Tasks for: 18 - Transformers

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

QA Tasks for: 9 - Autoencoders

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

QA Tasks for: 12 - Instance Segmentation

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

Using device-agnostic code in PyTorch

Could you please make the code device-agnostic so it can also run on Apple silicon Macbook? The current version of the code gives plenty of errors when running it on Mac.

Tasks

QA Tasks for: 8 - Pre-trained Networks

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

QA Tasks for: 21 - Genetic Algorithms

  • Copyedit
  • Check for Links to Learn, add if necessary, ensure they are instrumented
  • Knowledge Checks
  • Challenge
  • Any art? propose if necessary
  • Assignment
  • Pre-Quiz
  • Post-Quiz

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