datawhalechina/statistical-learning-method-solutions-manual resource

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2080 stars · 250 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

An open-source solutions manual for the exercise problems in Li Hang's textbooks 'Statistical Learning Methods' and 'Machine Learning Methods', published as an online VitePress book with Jupyter Notebook sources. It provides detailed derivations, errata, and Python/PyTorch implementations of algorithms like perceptron, decision trees, SVM, EM, and Transformer.

Use cases

• solutions to exercises in statistical learning methods textbook
• learn machine learning fundamentals with worked examples
• python implementations of svm decision tree em algorithm
• study guide for machine learning exams and interviews
• understand math derivations of classic ml algorithms
• hands-on pytorch examples of cnn rnn transformer

When to choose

• you are studying Li Hang's textbooks and want step-by-step exercise solutions
• you want runnable Python/PyTorch code alongside theory derivations
• you are a beginner preparing for exams or interviews on ML fundamentals

When to avoid

• you need a production machine-learning library or framework
• you want a complete, polished resource - this is an alpha, partially finished build
• you need coverage of topics beyond the textbook's scope

Facets

learning-resource · maturity experimental

machine-learning data-science documentation machine-learning deep-learning tutorials education python cross-platform jupyter-notebook exercise-solutions textbook-companion statistical-learning pytorch vitepress chinese education web

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