machine learning course notes / educational resources
Chinese-language study notes (with transcripts and slides) for Andrew Ng's Stanford/Coursera machine learning course.
Solves the same problem with a different architecture or at a different layer. Expect to rewrite the integration.
Choose this when you want a deep-learning-focused interactive textbook with PyTorch code instead of general course notes.
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
Choose this when you want hands-on Octave/MATLAB code examples of ML algorithms alongside theory.
🤖 MatLab/Octave examples of popular machine learning algorithms with code examples and mathematics being explained
Choose this when you want Python interactive demos and math explanations instead of static notes.
🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained
Choose this when you prefer a structured daily code challenge in Chinese over course notes.
100-Days-Of-ML-Code中文版
Choose this when you want a structured daily code challenge in English over course notes.
100 Days of ML Coding
Choose this when you want a modern practical book with fastai and a top-down approach.
The fastai book, published as Jupyter Notebooks
Choose this when you want a Chinese Q&A compendium covering ML/DL theory for interview prep.
深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06
Choose this when you need a Chinese ML/NLP knowledge and code reference for job interviews.
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。
Choose this when you want a focused book on LLMs rather than general ML course notes.
A book for Learning the Foundations of LLMs
Choose this when you want a step-by-step PyTorch implementation guide for LLMs.
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
These projects were analysed and named Coursera-ML-AndrewNg-Notes among their alternatives. The relationship is not symmetric — how Coursera-ML-AndrewNg-Notes rates them is a separate judgement, made when Coursera-ML-AndrewNg-Notes is analysed in its own right.
calls Coursera-ML-AndrewNg-Notes “Chinese notes for Andrew Ng's ML course, narrower and tied to that course.”
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。