[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9657":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":19,"rankGlobal":10,"rankLanguage":10,"license":20,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":21,"hasPages":21,"topics":23,"createdAt":10,"pushedAt":10,"updatedAt":29,"readmeContent":30,"aiSummary":31,"trendingCount":16,"starSnapshotCount":16,"syncStatus":32,"lastSyncTime":33,"discoverSource":34},9657,"MLAlgorithms","rushter\u002FMLAlgorithms","rushter","Minimal and clean examples of machine learning algorithms implementations","",null,"Python",10983,1755,419,10,0,6,13,44.73,"MIT License",false,"master",[24,25,26,27,28],"deep-learning","machine-learning","machine-learning-algorithms","neural-networks","python","2026-06-12 02:02:10","# Machine learning algorithms\nA collection of minimal and clean implementations of machine learning algorithms.\n\n### Why?\nThis project is targeting people who want to learn internals of ml algorithms or implement them from scratch.  \nThe code is much easier to follow than the optimized libraries and easier to play with.  \nAll algorithms are implemented in Python, using numpy, scipy and autograd.  \n\n### Implemented:\n* [Deep learning (MLP, CNN, RNN, LSTM)](mla\u002Fneuralnet)\n* [Linear regression, logistic regression](mla\u002Flinear_models.py)\n* [Random Forests](mla\u002Fensemble\u002Frandom_forest.py)\n* [Support vector machine (SVM) with kernels (Linear, Poly, RBF)](mla\u002Fsvm)\n* [K-Means](mla\u002Fkmeans.py)\n* [Gaussian Mixture Model](mla\u002Fgaussian_mixture.py)\n* [K-nearest neighbors](mla\u002Fknn.py)\n* [Naive bayes](mla\u002Fnaive_bayes.py)\n* [Principal component analysis (PCA)](mla\u002Fpca.py)\n* [Factorization machines](mla\u002Ffm.py)\n* [Restricted Boltzmann machine (RBM)](mla\u002Frbm.py)\n* [t-Distributed Stochastic Neighbor Embedding (t-SNE)](mla\u002Ftsne.py)\n* [Gradient Boosting trees (also known as GBDT, GBRT, GBM, XGBoost)](mla\u002Fensemble\u002Fgbm.py)\n* [Reinforcement learning (Deep Q learning)](mla\u002Frl)\n\n\n### Installation\n```sh\n        git clone https:\u002F\u002Fgithub.com\u002Frushter\u002FMLAlgorithms\n        cd MLAlgorithms\n        pip install scipy numpy\n        python setup.py develop\n```\n### How to run examples without installation\n```sh\n        cd MLAlgorithms\n        python -m examples.linear_models\n```\n### How to run examples within Docker\n```sh\n        cd MLAlgorithms\n        docker build -t mlalgorithms .\n        docker run --rm -it mlalgorithms bash\n        python -m examples.linear_models\n```\n### Contributing\n\nYour contributions are always welcome!  \nFeel free to improve existing code, documentation or implement new algorithm.  \nPlease open an issue to propose your changes if they are big enough.  \n","该项目提供了一系列机器学习算法的简洁且干净的实现。核心功能包括深度学习（如MLP、CNN、RNN、LSTM）、线性回归、逻辑回归、随机森林、支持向量机等，所有算法均使用Python编写，并基于numpy、scipy和autograd库实现。项目旨在帮助那些希望深入了解机器学习算法内部机制或从零开始构建算法的学习者，代码易于理解和修改。适用于教育场景、个人学习以及对已有优化库感到困惑的研究人员。",2,"2026-06-11 03:24:00","top_topic"]