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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
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| 2026-08-02 | 1423 |
| 2026-08-06 | 1424 |
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# 机器学习原理 --- 机器学习原理笔记整理. Gitbook地址[https://shunliz.gitbooks.io/machine-learning/content/](https://shunliz.gitbooks.io/machine-learning/content/) 前半部分关注数学基础，机器学习和深度学习的理论部分，详尽的公式推导。 后半部分关注工程实践和理论应用部分 **内容基本都是从互联网上扒来的，侵权的话联系：[email protected]。** [如何贡献？](/CONTRIBUTING.md) # 赞助 如果您觉得这个资料还不错，您也可以打赏一下。  由于我可爱的女儿出生，最近半年这个仓库都没有更新。昨天收到可爱的T\*o同学的打赏，提醒我要坚持更新。 | 姓名 | 金额 | | :--- | :--- | | T\*o | 6.66 | --- * [前言](README.md) * [第一部分 数学基础](math/math.md) * [第一章 数学分析](math/analytic/introduction.md) * [常用函数](math/analytic/common-function.md) * [牛顿法](math/analytic/niudun.md) * [梯度下降](math/analytic/gradient_descent.md) * [最小二乘](math/analytic/least-square.md) * [拉格朗日乘子法（Lagrange Multiplier\) 和KKT条件](math/analytic/lagelangri-kkt.md) * [凸优化](math/analytic/tuyouhua.md) * [数值计算](math/analytic/shu-zhi-ji-suan.md) * [第二章 概率论和统计](math/probability.md) * [统计学习方法概论](math/probability/prob-methodology.md) * [最大似然估计](math/probability/mle.md) * [蒙特卡罗方法](math/probability/mcmc1.md) * [马尔科夫链](math/probability/markov-chain.md) * [MCMC采样和M-H采样](math/probability/mcmc-mh.md) * [Gibbs采样](math/probability/gibbs.md) * [第三章 线性代数和矩阵](math/linear-matrix/linear-matrix.md) * [LAPACK](math/linear-matrix/lapack.md) * [特征值与特征向量](math/linear-matrix/tezhengzhihetezhengxiangliang.md) * [第二部分 机器学习](ml/ml.md) * [第四章 机器学习基础](ml/pythonml.md) * [机器学习库（numpy,scikit,scipy）](ml/pythonml/ji-qi-xue-xi-ku.md) * [机器学习库（Pandas,pySpark）](ml/pythonml/ji-qi-xue-xi-ku2.md) * [机器学习库（tensorflow, keras）](ml/pythonml/ji-qi-xue-xi-ku3.md) * [机器学习库（scipy,matplotlib）](ml/pythonml/ji-qi-xue-xi-ku-ff08-scipy-matplotlib.md) * [模型度量](ml/pythonml/ml-metrics.md) * [交叉验证](math/analytic/cross-validation.md) * [生成模型和判别模型](ml/pythonml/gen-descri.md) * [机器学习中的距离](ml/pythonml/distance.md) * [机器翻译模型度量](ml/pythonml/ji-qi-fan-yi-mo-xing-du-liang.md) * [模型选择](ml/pythonml/mo-xing-xuan-ze.md) * [常用激活函数总结](ml/pythonml/chang-yong-ji-huo-han-shu-zong-jie.md) * [深度学习损失函数](ml/pythonml/shen-du-xue-xi-sun-shi-han-shu.md) * [第六课：数据清洗和特征选择](ml/clean-feature/cleanup-feature.md) * [PCA](ml/clean-feature/pca.md) * [ICA](ml/clean-feature/ica.md) * [scikit-learn PCA](ml/clean-feature/scikit-pca.md) * [线性判别分析LDA](ml/clean-feature/xian-xing-pan-bie-fen-xi-lda.md) * [用scikit-learn进行LDA降维](ml/clean-feature/scikit-lda.md) * [奇异值分解\(SVD\)原理与在降维中的应用](ml/clean-feature/svd.md) * [局部线性嵌入\(LLE\)原理](ml/clean-feature/lle.md) * [scikit-learn LLE](ml/clean-feature/scikit-lle.md) * [Spark特征提取](ml/clean-feature/spark-fextract.md) * [数据图示分析](ml/pythonml/shu-ju-tu-shi-fen-xi.md) * [异常数据监测](ml/clean-feature/outlier-detect.md) * [数据预处理](ml/clean-feature/datapreprocess.md) * [特征工程](ml/clean-feature/te-zheng-gong-cheng.md) * [区分度评估指标\(KS\)](ml/clean-feature/qu-fen-du-ping-gu-zhi-680728-ks.md) * [变量编码方法](ml/clean-feature/bian-liang-bian-ma-fang-fa.md) * [One-hot编码](ml/clean-feature/one-hot.md) * [数据处理：离散型变量编码及效果分析](ml/clean-feature/shu-ju-chu-li-ff1a-li-san-xing-bian-liang-bian-ma-ji-xiao-guo-fen-xi.md) * [变量分箱方法](ml/clean-feature/bian-liang-fen-xiang-fang-fa.md) * [最优卡方分箱法](ml/clean-feature/bian-liang-fen-xiang-fang-fa/zui-you-qia-fang-fen-xiang-fa.md) * [Best-KS分箱方法](ml/clean-feature/bian-liang-fen-xiang-fang-fa/best-ksfen-xiang-fang-fa.md) * [最优IV分箱方法](ml/clean-feature/bian-liang-fen-xiang-fang-fa/zuiyou-iv-fen-xiang-fang-fa.md) * [基于树的最优分箱方法](ml/clean-feature/bian-liang-fen-xiang-fang-fa/ji-yu-shu-de-zui-you-fen-xiang-fang-fa.md) * [变量选择](ml/clean-feature/bian-liang-xuan-ze.md) * [特征筛选](ml/clean-feature/te-zheng-shai-xuan.md) * [spark特征选择](ml/clean-feature/spark-fselect.md) * [过滤法](ml/clean-feature/bian-liang-xuan-ze/guo-lv-fa.md) * [包装法](ml/clean-feature/bian-liang-xuan-ze/bao-zhuang-fa.md) * [嵌入法](ml/clean-feature/bian-liang-xuan-ze/qian-ru-fa.md) * [第七课： 回归](ml/regression/regression.md) * [1. 线性回归](ml/regression/linear-regression.md) * [10.最大熵模型](ml/regression/max-entropy.md)
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:07ad38355d87808e, llm:Repo name 'Machine-Learning' and description '机器学习原理' (meaning 'principles of machine learning')