What is this?
A from-scratch Python implementation of every algorithm covered in Li Hang's book 'Statistical Learning Method' (统计学习方法), with heavily commented code that maps directly to the formulas in the book.
A from-scratch Python implementation of every algorithm covered in Li Hang's book 'Statistical Learning Method' (统计学习方法), with heavily commented code that maps directly to the formulas in the book.Is this a library I import, or standalone example scripts?It's a collection of standalone scripts, one folder per algorithm/chapter (e.g. SVM/SVM.py, HMM/HMM.py), not a packaged library you pip-install.
It's a collection of standalone scripts, one folder per algorithm/chapter (e.g. SVM/SVM.py, HMM/HMM.py), not a packaged library you pip-install.How do I run one of the algorithm implementations?Clone the repo and run the corresponding script directly with Python, for example python perceptron/perceptrondichotomy.py; each chapter's code lives in its own folder.
Clone the repo and run the corresponding script directly with Python, for example python perceptron/perceptrondichotomy.py; each chapter's code lives in its own folder.Do I need to do anything with the dataset before running the scripts?Yes — the included Mnist dataset is shipped as a compressed csv (because the uncompressed file is 107MB), so you must unzip the archive inside the Mnist folder before running scripts that use it.
Yes — the included Mnist dataset is shipped as a compressed csv (because the uncompressed file is 107MB), so you must unzip the archive inside the Mnist folder before running scripts that use it.Can I reuse this code commercially?No — the project content is licensed under CC BY-NC-SA 4.0, which is non-commercial, so you can share and adapt it but not sell or use it commercially.
No — the project content is licensed under CC BY-NC-SA 4.0, which is non-commercial, so you can share and adapt it but not sell or use it commercially.Is this meant for production use or for learning?It's a learning companion to the book — each implementation favors clarity and formula traceability over performance, aimed at students studying classic statistical learning methods.
It's a learning companion to the book — each implementation favors clarity and formula traceability over performance, aimed at students studying classic statistical learning methods.Which algorithms from the book are implemented?Supervised: Perceptron, KNN, Naive Bayes, Decision Tree, Logistic Regression, Maximum Entropy, SVM, AdaBoost, EM, and HMM. Unsupervised: K-means, Hierarchical Clustering, PCA, LSA, PLSA, LDA, and PageRank.
Supervised: Perceptron, KNN, Naive Bayes, Decision Tree, Logistic Regression, Maximum Entropy, SVM, AdaBoost, EM, and HMM. Unsupervised: K-means, Hierarchical Clustering, PCA, LSA, PLSA, LDA, and PageRank.Is there more explanation than just code comments?Yes, the author wrote companion blog posts walking through the theory for most chapters (e.g. 'Statistical Learning Method | SVM principle analysis and implementation'), linked directly in the README.
Yes, the author wrote companion blog posts walking through the theory for most chapters (e.g. 'Statistical Learning Method | SVM principle analysis and implementation'), linked directly in the README.Is this project still being actively developed?The author mentioned a book deal (with People's Posts and Telecommunications Press) and plans to refactor the code in a separate 'book' branch as of a Feb 2022 update, but updates have otherwise been sporadic.
The author mentioned a book deal (with People's Posts and Telecommunications Press) and plans to refactor the code in a separate 'book' branch as of a Feb 2022 update, but updates have otherwise been sporadic.What are similar projects?lihang-code, Book4_Power-of-Matrix, 100-Days-Of-ML-Code, Machine-Learning, ailearning, mml-book.github.io, Ai-Learn, maths-cs-ai-compendium
lihang-code, Book4_Power-of-Matrix, 100-Days-Of-ML-Code, Machine-Learning, ailearning, mml-book.github.io, Ai-Learn, maths-cs-ai-compendiumWhich topics is it in?svm, code, machine-learning-algorithms, mathematics, algorithm, statistics, algorithms, data-science, book, beginner-friendly, learning-resources, tutorial
svm, code, machine-learning-algorithms, mathematics, algorithm, statistics, algorithms, data-science, book, beginner-friendly, learning-resources, tutorial