microsoft/ai-edu

★ 14,066HTMLAudience · generalComplexity · 2/5Setup · easy

Mindmap

mindmap
  root((ai-edu))
    Content sections
      Foundational tutorials
      Practical case studies
      Practice projects
    Topics covered
      Python basics
      Neural networks
      Machine learning
      Computer vision
      NLP and speech
    Case study examples
      Digit recognition
      Comic translation
      AI couplet generation
      Time-series forecast
    Audience
      Chinese students
      Teachers
      IT professionals
    Community
      Microsoft Research Asia
      Open contributions

mindmap root((ai-edu)) Content sections Foundational tutorials Practical case studies Practice projects Topics covered Python basics Neural networks Machine learning Computer vision NLP and speech Case study examples Digit recognition Comic translation AI couplet generation Time-series forecast Audience Chinese students Teachers IT professionals Community Microsoft Research Asia Open contributions

Click or tap to explore — scroll the page freely

Things people build with this

USE CASE 1

Follow the structured foundational tutorials to learn Python and neural network basics from the ground up in Chinese.

USE CASE 2

Work through the handwritten digit recognition case study as a first practical computer vision project.

USE CASE 3

Use the natural language understanding and speech case studies to build practical NLP and voice projects with guided examples.

USE CASE 4

Run team-based practice projects from the competition and internship exercises to apply AI skills to larger real-world problems.

Tech stack

HTMLPythonJupyter Notebook

Getting it running

Difficulty · easy Time to first run · 30min

All content is written in Chinese, non-Chinese readers will need translation assistance to follow the tutorials.

No license information is mentioned in the explanation.

In plain English

This repository is an open-source AI education community created by Microsoft Research Asia. It is written entirely in Chinese and is aimed at Chinese students, teachers, and IT professionals who want to learn artificial intelligence from the ground up. The content is organized into three sections. The first is foundational tutorials, which cover topics such as Python programming basics, how neural networks work, classic machine learning algorithms, modern software engineering, and AI systems. These are structured courses meant to build knowledge step by step. The second section is practical case studies, which are hands-on projects covering areas like natural language understanding, computer vision, and speech. Examples include building a model to recognize handwritten digits, translating comics, generating AI-composed couplets (a traditional Chinese poetry form), and doing time-series forecasting. The third section is practice projects, which are larger team-based exercises carried out as part of student competitions and internship programs. The project is supported by both the research and academic partnerships teams at Microsoft Research Asia. There is a companion website where the content can be read in a more structured format. The repository also links to a sister project on AI systems, kept as a submodule that stays in sync with its own repository. Contributions are welcome. The project encourages teachers and learners to share their own resources and experiences. Issues are used to track monthly update plans and bug reports, and pull requests are accepted for fixes and new content.

Copy-paste prompts

Prompt 1

I'm following the Microsoft AI-edu curriculum. Explain the foundational neural network content in plain English, then give me a Python exercise to build a simple two-layer network from scratch using only NumPy.

Prompt 2

Using the AI-edu handwritten digit recognition case study as a reference, write Python code that loads the MNIST dataset, trains a simple CNN, and prints the test accuracy.

Prompt 3

Translate the outline of the AI-edu foundational machine learning curriculum from Chinese into English and suggest an order to work through the topics for a complete beginner.

Prompt 4

Based on the AI-edu time-series forecasting case study, write Python code using a simple LSTM to forecast the next 7 days of values from a CSV file of daily readings.

Prompt 5

I want to contribute a new tutorial to the Microsoft AI-edu repository. Show me the file and folder structure I should follow based on the existing content organization.

Open on GitHub → Explain another repo

← microsoft on gitmyhub — every repo by this author, as a profile.

Verify against the repo before relying on details.