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2020mcm

2020美赛的个人参赛经验，附美赛备战资料以及C题M奖论文

2021mcm-d-outstanding-winner-and-informs-award

开源本人参加2021美赛时的部分代码，感谢我的两位队友与训练营的老师前辈们，大家共同努力才能斩获顶奖。

advanced_probability_theory

Materials for Probability Theory and Modelling

ailearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

beautiful-visualization-with-python

python数据可视化之美

beautiful-visualization-with-r

《R语言数据可视化之美》配套代码

clash_for_windows_pkg

A Windows/macOS GUI based on Clash

computer_science

some algorithms

coursera-sna

Lecture Materials & Video (march 2013)

deeplearningbook-chinese

Deep Learning Book Chinese Translation

dlsa_r

esl

notes for the Elements of Statistical Learning

esl-cn

The Elements of Statistical Learning (ESL)的中文翻译、代码实现及其习题解答。

excel-chart-plugin-easycharts

Excel图表插件：EasyCharts1.0

gnar_code

Code for the GNAR model (by Xuening Zhu).

how-to-ask-questions-the-smart-way

本文原文由知名 Hacker Eric S. Raymond 所撰寫，教你如何正確的提出技術問題並獲得你滿意的答案。

ivreghdfe

Run IV/2SLS with many levels of fixed effects (i.e. ivreg2+reghdfe)

latex-ppt-template

Seven awesome latex ppt templates for researchers or students.

lihang-code

《统计学习方法》的代码实现

machine-learning-learning-notes

周志华《机器学习》又称西瓜书是一本较为全面的书籍，书中详细介绍了机器学习领域不同类型的算法(例如：监督学习、无监督学习、半监督学习、强化学习、集成降维、特征选择等)，记录了本人在学习过程中的理解思路与扩展知识点，希望对新人阅读西瓜书有所帮助！

mathmodel

研究生数学建模，本科生数学建模、数学建模竞赛优秀论文，数学建模算法，LaTeX论文模板，算法思维导图，参考书籍，Matlab软件教程，PPT

meme.decomposition

This package makes MEME index decompositions

ml-nlp

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

nar

The code for network autoregression model (NAR)

networkanalysis

Coursera-SNA

pm2.5-attr-mort

An Integrated Calculate Program for PM2.5 Health Burden Assessment

portal_node_code

r-d3-sna-course-example

A simple example using R and D3.js for show the examples of SNA Course in Coursera

rweibo

Rweibo case

scad

Fit coefficients paths for MCP- or SCAD-penalized regression models over a grid of values for the regularization parameter lambda. Fits linear and logistic regression models, with option for an additional L2 penalty.