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Projects
classical_learn
【Machine Learning, classical_learn】Linear regression
【Machine Learning, classical_learn】Logistic regression
Jupyter Notebooks
nbviewer is suggested. nbviewer is very stable and fast, and it support link for table content.
【Physics, feynman】费恩曼图（新）
【Physics, pyfeyn】费恩曼图（旧）
【python, physics】光被随机位置的粒子散射的干涉项的模拟
【python, numpy】QQ AppUtil.dll从浏览器历史读取用户某些域名下的搜索数据，人工暴力破解最后一个域名
【python, numpy】hash_unqiue.unique: a faster implementation of numpy.unique
【Machine Learning, LSA, pLSA, LDA】LSA, pLSA和LDA：实现和演示 [nbviewer]
【Machine Learning, pytorch】评测手头几台pc的pytorch的性能：更大的模型
【Machine Learning, pytorch】评测手头几台pc的pytorch的性能
【Machine Learning】K臂老虎机：实现和演示 [nbviewer]
【Python】any, all and empty array
[nbviewer]
【Machine Learning】LearnLog: Jupyter Notebook中记录traning进度的小工具 [nbviewer]
【Machine Learning】回归模型比较：演示
【Machine Learning】sklearn KernelRidge一点笔记
【Machine Learning】局部回归：实现和演示
【Machine Learning】k最邻近与kd树：实现和演示
【Machine Learning】高斯过程与过早停止：实验演示
【Machine Learning】高斯过程与超参数优化：实验演示
【Machine Learning】高斯过程回归：实现
【Machine Learning】线性回归与主成分分析与正则化
【Machine Learning】B样条作为概率密度函数，以及kernel
【Machine Learning】多维高次（非自然）样条回归
【Machine Learning】多维二次自然样条回归
【Numpy】一些Numpy函数的速度比较 [nbviewer]
【c++】c++14和c++17练习
【Machine Learning】多项式拟合中的偏差和方差
【Machine Learning】深入支持向量机
【Machine Learning】潜在语义模型：潜在语义分析，概率潜在语义分析和潜在狄利克雷分配
【Machine Learning】使用Metropolis-Hasting方法产生高斯随机数（非对称建议分布）
【Optics】Simple ray tracing in optics
【Mathematical Model】New York Covid19
【Machine Learning】Gaussian Mixture Implementation
【Statistics】Estimator of mean, variance, third central moment, and fourth central moment
【Python, Numpy, Numba, Cython, PyPy, Julia】A speed comparison between python numpy, numba, cython, pypy, julia, java and c++ in numerical compulation
【Machine Learning】Naive Bayes Implementation
【Python】110道python练习题。
【Python】我的Python笔记
【Bash】我的bash笔记
【Machine Learning】GBDT Implementation
【Statistics】bootstrap Experiments
【Machine Learning】Kmeans Implementation
【Machine Learning】Support Vector Machine Implementation