AiLearning-Theory-Applying: Annotated Chinese AI and ML Learning Repository

An Annotated AI Curriculum for Chinese-Speaking Practitioners

AiLearning-Theory-Applying is a GitHub repository of Jupyter Notebooks that covers AI from mathematical prerequisites to competition-winning solutions. The README describes its goal as letting every reader understand and reproduce the material. Dense annotations in the notebooks are the primary mechanism: the README uses the phrase "力求每一位能看懂并复现" (roughly: to ensure every reader can understand and reproduce), signaling that the intended audience is someone building toward real implementation, not just reading an overview.

The repository is in Chinese. Course comments, explanations, and section headings use Chinese, while code, package names, and technical identifiers remain in their standard English forms. The target audience is Chinese-speaking engineers and students entering the AI field who want learning material that connects mathematical theory, code implementation, and practical competition experience.

Mathematical Foundations: The Prerequisite Section

The 必备数学基础 (Essential Math Foundations) section opens the repository and covers the math that appears throughout the rest of the material. The README lists the topics: advanced mathematics basics, calculus (微积分), Taylor series, linear algebra basics, random variables, probability theory, common distributions in data science, kernel functions, entropy and activation functions, regression analysis, hypothesis testing, correlation analysis, variance analysis, K-Means algorithm, and Bayesian analysis.

Each topic has a corresponding directory under `notebook_必备数学基础/` with Jupyter Notebooks. The math is presented alongside code examples, not as pure theory. The repository's design assumes that a reader who cannot execute and verify the math in code will struggle with the subsequent machine learning sections, so the notebooks are intended to be run interactively.

The `必备数学基础.md` file at the repository root appears to be the main text for this section, while the `notebook_必备数学基础/` directory holds the runnable notebooks.

Transformer Architecture in Eight Chapters

The 人人都能看懂的Transformer (Transformer Everyone Can Understand) section covers the Transformer architecture in eight chapters. The README lists the chapter titles: Transformer network architecture, text vectorization, positional encoding, multi-head attention with QK matrix multiplication, the full multi-head attention pipeline, numerical scaling, the feedforward neural network, and the final output layer.

Each chapter is a markdown file under the `人人都能看懂的Transformer/` directory. The goal of this section, as signaled by the title, is to break down the Transformer into pieces that can be followed without a deep pre-existing ML background. The 2024 Financial Industry LLM Challenge competition solution is also in this section under `LLM大模型竞赛实战_优胜解决方案/`, providing a concrete applied example alongside the theory.

Machine Learning Competition Solutions and Deep Learning Basics

The 机器学习竞赛实战_优胜解决方案 (ML Competition Winning Solutions) directory collects annotated solutions from machine learning competitions. The README uses this as one of the four main categories alongside machine learning algorithm derivations, a deep learning introduction, and competition techniques.

The `机器学习算法原理及推导/` directory covers ML algorithm theory and derivations. The `深度学习入门/` directory covers deep learning fundamentals. The `竞赛优胜技巧/` directory holds competition-specific techniques.

The most recent GitHub release, "embodied-simulation-baseline" (2026-09-03), is described in the release title as including 67 or more competition reproductions. This release corresponds to the EmbodiedAI section of the repository under `EmbodiedAI/`, which appears to be the most actively updated area based on the release date and the repository's last push on 2026-09-19.

NLP, BERT, and the Embodied AI Section

The NLP通用框架BERT项目实战 directory covers BERT and NLP applications. BERT (Bidirectional Encoder Representations from Transformers) is covered as a practical project implementation rather than a pure theory walkthrough, following the pattern of the rest of the repository.

The EmbodiedAI section is the newest addition and is where the embodied simulation baseline notebooks live. Embodied AI refers to AI systems that interact with physical or simulated environments through sensing and actuation. The release notes name this a baseline for an embodied simulation synthesis competition with 67 or more historical candidate reproductions, making it a reference for practitioners working in that area.

The `assets/` directory at the repository root holds images and supporting files used across the notebooks.

How to Use the Repository: Navigation and No Centralized Entry Point

The README does not provide install steps, an environment specification file, or a setup script. The repository is designed to be accessed in two ways: browsing on GitHub directly, where the README links each section, or cloning the repository and opening notebooks in a local Jupyter environment.

Readers who want to run the notebooks need to set up a Python environment with Jupyter installed and install any packages that individual notebooks import. Because the repository spans multiple topic areas over several years of development, the exact package requirements vary by notebook. There is no single `requirements.txt` or `environment.yml` file listed at the repository root.

The most practical navigation path is through the README's section links, which lead to specific subdirectory READMEs and notebook files. The top-level repository entries show the main directories: `EmbodiedAI/`, `LLM大模型竞赛实战_优胜解决方案/`, `NLP通用框架BERT项目实战/`, `notebook_必备数学基础/`, `人人都能看懂的Transformer/`, `机器学习竞赛实战_优胜解决方案/`, `机器学习算法原理及推导/`, `深度学习入门/`, and `竞赛优胜技巧/`.

Limitations: Chinese Only, No Curriculum Structure, and Comparison with Fast.ai

All explanatory text in the notebooks is in Chinese. Code, package names, and API identifiers remain in English, as they are everywhere, but the explanations and annotations are not available in other languages. Engineers who do not read Chinese will find only the code cells useful.

The repository does not follow a linear curriculum with prerequisites enforced. There are no quizzes, grading, or structured progression. A reader can enter at any section. The trade-off is flexibility: the material can be used as a reference for one topic without working through the entire repository.

Fast.ai is an English-language AI learning resource that provides structured courses, video lectures, and Jupyter Notebooks covering deep learning and machine learning. The design philosophy is the opposite of AiLearning-Theory-Applying in one respect: fast.ai starts from application and works toward theory, while AiLearning-Theory-Applying starts from mathematical prerequisites and builds toward application. Readers who prefer the top-down approach or need English content should look at fast.ai. Readers who want to start from math and work through Chinese-language annotations alongside competition code will find AiLearning-Theory-Applying covers ground that fast.ai does not.

Editorial conclusion

AiLearning-Theory-Applying is well-suited for Chinese-speaking engineers and students who want to study AI theory alongside working code and competition solutions, and who want annotated notebooks they can run and modify. The repository does not provide an install script or environment file; readers need to set up a Python and Jupyter environment on their own and verify that notebook dependencies are available. Engineers who need English-language content, step-by-step course videos, or a structured curriculum with automatic grading will not find those here. The last push was on 2026-09-19.

Frequently asked questions

Official sources

1. ben1234560/AiLearning-Theory-Applying on GitHub
2. Issues
3. License: MIT
4. README
5. Releases