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ben1234560/AiLearning-Theory-Applying

A Chinese-language course-style repository covering AI math basics, machine learning, deep learning, Transformer, BERT and competition case studies, with annotated code and datasets.

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What is it?

AiLearning-Theory-Applying is a Chinese-language learning repository for people getting started with AI. It walks through the math foundations (calculus, linear algebra, probability and statistics), classic machine learning algorithms such as KMeans and Bayesian analysis, deep learning topics from neural networks through CNN, RNN and LSTM, and modern NLP with Transformer and BERT.

Beyond theory notes, the repository contains hands-on projects built from Kaggle and Chinese data competitions, including credit card fraud detection, road travel-time prediction and Chinese sentiment analysis with BERT. Many projects ship with their datasets and heavily commented code so that readers can run and reproduce the results.

The content is organized as a chaptered study path, with Markdown articles alongside Jupyter Notebooks. It is aimed at learners who want to follow along and reproduce examples rather than read abstract surveys.

Key features

  • 01Covers math foundations, classic machine learning, deep learning and Transformer/BERT theory in one path.
  • 02Provides competition-based projects such as credit card fraud detection and travel-time prediction.
  • 03Includes datasets and commented code for many projects so examples can be reproduced.
  • 04Offers a Chinese BERT sentiment analysis tutorial with the original code.
  • 05Mixes Markdown explanations with Jupyter Notebooks.

Use cases

  • Learning the math behind machine learning before writing models.
  • Following step-by-step Kaggle-style projects to practice data modeling.
  • Studying how Transformer and BERT work with annotated explanations.
  • Preparing for Chinese-language AI data competitions with worked examples.

Things to know

  • The content is written in Chinese.
  • The README says the material is not for commercial use, which conflicts with the MIT license in the metadata.

In collections

Natural language processing tools & libraries

Topics

#machine-learning#deep-learning#nlp#bert#transformer#tutorial#jupyter-notebook#kaggle#ai#dataming

FAQ

What is AiLearning-Theory-Applying?
A Chinese-language course-style repository covering AI math basics, machine learning, deep learning, Transformer, BERT and competition case studies, with annotated code and datasets. AiLearning-Theory-Applying is a Chinese-language learning repository for people getting started with AI. It walks through the math foundations (calculus, linear algebra, probability and statistics), classic machine learning algorithms such as KMeans and Bayesian analysis, deep learning topics from neural networks through CNN, RNN and LSTM, and modern NLP with Transformer and BERT.
Is AiLearning-Theory-Applying free to use? What license does it have?
AiLearning-Theory-Applying is open source under the MIT license. Check the license terms before using it in commercial products.
Is AiLearning-Theory-Applying still maintained?
Actively maintained. The last commit to AiLearning-Theory-Applying was on Oct 18, 2026.
What can I use AiLearning-Theory-Applying for?
Learning the math behind machine learning before writing models. Following step-by-step Kaggle-style projects to practice data modeling. Studying how Transformer and BERT work with annotated explanations. Preparing for Chinese-language AI data competitions with worked examples.
Where can I get AiLearning-Theory-Applying?
The source code and installation instructions are on GitHub: https://github.com/ben1234560/AiLearning-Theory-Applying

Summary generated by AI from the project's README. GitHub stats refresh daily.

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