├── Postgraduate.md ├── README.md ├── books.md └── papers.md /Postgraduate.md: -------------------------------------------------------------------------------- 1 | ## 读研的基本知识 2 | 3 | ### 一些网站收集 4 | 5 | --- 6 | 7 | * [导师评价网](https://www.mysupervisor.org/)：知名院校的导师评价信息（仅供参考） 8 | 9 | 10 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # 大数据方向学习路线 2 | 3 | ```python 4 | 由于 5 | 个人精力有限 6 | 需要优化路线结构 7 | 团队力量胜过个人力量 8 | 故 9 | 需要有志愿者来和我一起完善这个工作 10 | 可以留言 11 | ``` 12 | 13 | 14 | 15 | #### 编程语言 16 | 17 | - - - 18 | 19 | `python` `Java` `C++` `scala` 20 | 21 | #### 数据处理 22 | 23 | - - - 24 | 25 | `numpy` `pandas` `matplotlib` 26 | 27 | 28 | #### 之后会给出的路线方案 29 | 30 | ---- 31 | 32 | `模型评估` 33 | 34 | `不平衡数据` 35 | 36 | `序列数据` 37 | 38 | `高维数据` 39 | 40 | `CTR类型` 41 | 42 | `模型融合` 43 | 44 | `优化训练Tricks` 45 | 46 | `NLP及特征工程` 47 | 48 | `Pyspark` 49 | 50 | `爬虫` 51 | 52 | `云GPU` 53 | 54 | `优秀解决方案` 55 | 56 | `ML&DL&RL项目的demo` 57 | 58 | `大数据框架学习` 59 | 60 | `计算机视觉及其相关框架` 61 | 62 | `语音实践` 63 | 64 | `Python开发` 65 | 66 | `论文查找和项目实现` 67 | 68 | `tensorflowSpark` 69 | 70 | `docker` 71 | 72 | `deepFM` 73 | 74 | `工程模板` 75 | 76 | `智能算法` 77 | 78 | `OpenCV工程` 79 | 80 | `采样优化` 81 | 82 | `优化方法选取` 83 | 84 | ### 网站罗列 85 | 86 | ---- 87 | 88 | #### 工具 89 | 90 | * [markdown](https://www.appinn.com/markdown/#link)：markdown语法 91 | * [nbviewer-jupyter](http://nbviewer.jupyter.org/)：方便看github上的的ipynb文件 92 | * [Git图解](http://gitbeijing.com/) 93 | * [ProcessOn](https://www.processon.com/)：在线制作流程图 94 | * [gitbook](https://legacy.gitbook.com/)：gitbook手册制作 95 | * [各式网站收集](https://dodola.gitbooks.io/gitbook/content/)：一些不错的网站收集 96 | * [Python第三方包网站](https://www.lfd.uci.edu/~gohlke/pythonlibs/) 97 | * [devdoc](https://devdocs.io/)：各类文档收集 98 | * [nn-playgound](http://playground.tensorflow.org/)：神经网络训练场 99 | * [nn-visualize](http://www.emergentmind.com/neural-network)：神经网络可视化和简单实验 100 | * [nn可解释性](https://distill.pub/2018/building-blocks/)：隐藏层可解释性 101 | * [Visualizing-MNIST](https://colah.github.io/posts/2014-10-Visualizing-MNIST/)：MNIST可视化 102 | * [docker](https://github.com/llitfkitfk/docker-tutorial-cn)：虚拟化 103 | * [google dataset search](https://toolbox.google.com/datasetsearch)：数据集搜索（墙） 104 | 105 | #### 计算机专业课 106 | 107 | * [Cousera-计算机组成原理](https://www.bilibili.com/video/av12666021) 108 | 109 | #### C&C++ 110 | 111 | * [Cousera-C语言设计进阶](https://www.bilibili.com/video/av12637226?from) 112 | * [Cousera-C++程序设计](https://www.bilibili.com/video/av12638525) 113 | 114 | #### Python 115 | 116 | * [中文 Python 笔记](https://github.com/lijin-THU/notes-python) 117 | * [没有废话的python教程](https://www.bilibili.com/video/av5236569?from=search&seid=9511873466524619840) 118 | 119 | #### Java 120 | 121 | * [Java开发相关](https://github.com/PansonPanson/Java-Notes) 122 | 123 | #### 计算机视觉（CV） 124 | 125 | * [52cv](https://www.52cv.net/) ：CV社区 126 | * [Opencv](https://github.com/makelove/OpenCV-Python-Tutorial)：Opencv教程 127 | 128 | #### 文档翻译及教程 129 | 130 | * [ApacheCN](http://www.apachecn.org/)：优秀文档翻译社区 131 | * [Tensorfly](http://www.tensorfly.cn/home/) ：tensorflow教程和文档 132 | * [TensorflowNews](http://www.tensorflownews.com/)：tensorflow教程和文档 133 | * [磐创AI-tf-教程](http://panchuang.net/)：tensorflow教程 134 | * [Pytorch中文网](https://www.pytorchtutorial.com/)：PyTorch 中文网 135 | * [人工智能笔记](http://huaxiaozhuan.com/) ：机器学习、深度学习知识笔记 136 | * [AIQ](http://www.6aiq.com/)：机器学习社区 137 | 138 | #### NLP 139 | 140 | * [52nlp](http://www.52nlp.cn/) ：nlp社区 141 | 142 | #### 机器学习&深度学习&数据科学 143 | 144 | * [斯坦福《机器学习》笔记](https://github.com/zlotus/notes-LSJU-machine-learning)：课程笔记 145 | * [斯坦福《机器学习》Python实现](https://github.com/yoyoyohamapi/mit-ml)：代码实现 146 | * [莫烦的教程](https://github.com/MorvanZhou/tutorials)：莫烦python 147 | * [机器学习笔记梳理](https://feisky.xyz/machine-learning/)：不错的机器学习知识路线 148 | * [colah blog](https://colah.github.io/)：机器学习，深度学习相关博客 149 | * [深度学习500问](https://github.com/scutan90/DeepLearning-500-questions) ：面试常问 150 | * [数据科学笔记](https://github.com/donnemartin/data-science-ipython-notebooks)：data science 151 | * [Pyecharts-中文文档](http://pyecharts.org/#/zh-cn/)：pyecharts数据可视化 152 | * [用Python做科学计算](http://old.sebug.net/paper/books/scipydoc/index.html#) 153 | 154 | #### 数学知识基础和提高 155 | 156 | * [MIT线性代数笔记](https://github.com/apachecn/math) 157 | 158 | #### 数据比赛优秀解决方案或者技巧 159 | 160 | * [kaggle优秀解决方案收集](https://www.kaggle.com/shivamb/data-science-glossary-on-kaggle-updated/) 161 | * [关于特征的技巧（墙）](https://towardsdatascience.com/my-secret-sauce-to-be-in-top-2-of-a-kaggle-competition-57cff0677d3c)：[转载](http://www.sohu.com/a/273552971_129720) 162 | 163 | #### 推荐系统 164 | 165 | * [推荐系统论文](https://github.com/fuxuemingzhu/Summary-of-Recommender-System-Papers) 166 | 167 | #### 算法&数据结构 168 | 169 | * [北大Coursesa算法分析与设计](https://www.bilibili.com/video/av12681389) 170 | * [清华数据结构与算法-邓俊辉](https://www.bilibili.com/video/av22774520) 171 | 172 | #### 数学建模 173 | 174 | * [数学建模相关算法及MATLAB实现](https://github.com/HuangCongQing/Algorithms_MathModels) 175 | * [数学建模相关模板资源](https://github.com/zhanwen/MathModel) 176 | * [matlab教程-学堂在线](http://www.xuetangx.com/courses/course-v1:CSU+2018012601X+sp/about) 177 | 178 | #### 强化学习 179 | 180 | * [DeepMind&伦敦大学教程](https://www.youtube.com/watch?v=iOh7QUZGyiU&index=2&list=PLqYmG7hTraZDNJre23vqCGIVpfZ_K2RZs&t=0s) 181 | 182 | ### 程序设计&算法 183 | 184 | --- 185 | 186 | #### 在线训练网站 187 | 188 | * [牛客网](https://www.nowcoder.com/) 189 | * [赛码网](http://exercise.acmcoder.com) 190 | * [计蒜客](https://www.jisuanke.com/) 191 | 192 | #### Online Judge 193 | 194 | * [杭州电子科技大学](http://acm.hdu.edu.cn/) 195 | * [浙江大学](http://acm.zju.edu.cn/onlinejudge/) 196 | * [topcoder](https://accounts.topcoder.com) 197 | * [哈尔滨工业大学](http://acm.hit.edu.cn/) 198 | * [51nod](http://www.51nod.com/) 199 | * [PAT真题](https://www.nowcoder.com/pat) 200 | * [电子科技大学](https://acm.uestc.edu.cn) 201 | * [UOJ](http://uoj.ac/)：据说这个oj开源了 202 | * [OJ题型分类](http://www.pythontip.com/acm/problemCategory) 203 | 204 | ### 开发相关 205 | 206 | ---- 207 | 208 | #### Django 209 | 210 | * [Django教程](https://github.com/zmrenwu/django-blog-tutorial) 211 | * [基于Django的博客系统](https://github.com/liangliangyy/DjangoBlog) 212 | 213 | #### Android 214 | 215 | * [安卓学习笔记](https://github.com/GcsSloop/AndroidNote) 216 | 217 | ### 框架用户文档&教程 218 | 219 | * [Pytorch-zh](https://github.com/apachecn/pytorch-doc-zh) 220 | * [tensorflow官方中文](https://tensorflow.google.cn/tutorials/?hl=zh-cn) 221 | 222 | ### 图书配套源码 223 | 224 | --- 225 | 226 | * [《深度学习与计算机视觉》](https://github.com/frombeijingwithlove/dlcv_for_beginners) 227 | * [《OpenCV3编程入门》](https://github.com/QianMo/OpenCV3-Intro-Book-Src) 228 | * [《PyQt5快速开发与实战》](https://github.com/cxinping/PyQt5) 229 | * [《21个项目玩转深度学习———基于TensorFlow的实践详解》](https://github.com/hzy46/Deep-Learning-21-Examples) 230 | * [《第一行代码 第2版》](https://github.com/guolindev/booksource) 231 | * [《剑指Offer》](https://github.com/zhedahht/CodingInterviewChinese2) 232 | * [《Python Web开发实战》](https://github.com/dongweiming/web_develop) 233 | * [《跟老齐学Python系列》图书之《数据分析》](https://github.com/qiwsir/DataAnalysis) 234 | * [《跟老齐学Python系列》图书之《零基础学Python》](https://python.xiaoleilu.com/) 235 | * [《深度学习框架PyTorch：入门与实践》](https://github.com/chenyuntc/pytorch-book) 236 | 237 | ### 面试笔试 238 | 239 | --- 240 | 241 | * [互联网面试笔记](https://github.com/zhengjianglong915/note-of-interview) 242 | * [Python2面试](https://github.com/taizilongxu/interview_python) 243 | * [前端面试题](https://github.com/paddingme/Front-end-Web-Development-Interview-Question) 244 | * [Java&Android面试题](https://github.com/hadyang/interview) 245 | * [C/C++面试](https://github.com/huihut/interview) 246 | * [《剑指offer》Python实现](https://github.com/JushuangQiao/Python-Offer) 247 | * [《剑指offer》编程题](https://github.com/gatieme/CodingInterviews) 248 | * [知识点总结](https://github.com/it-interview/EasyJob) 249 | * [校招面试笔试高频题](https://github.com/NtZheng/NTAlgorithm) 250 | * [2018/2019/校招/春招/秋招](https://github.com/imhuay/Algorithm_Interview_Notes-Chinese) 251 | * [机器学习面试题](https://geekcircle.org/machine-learning-interview-qa/) 252 | * [BAT面试](https://github.com/lengyue1024/BAT_interviews) 253 | * [深度学习500问](https://github.com/scutan90/DeepLearning-500-questions) 254 | 255 | ### 优秀个人和组织收集 256 | 257 | ---- 258 | 259 | * 贾扬清：[个人主页](http://daggerfs.com/) 、[知乎主页](https://www.zhihu.com/people/jiayangqing/activities) 260 | * 周志华：[个人主页](http://cs.nju.edu.cn/zhouzh/) 、[团队主页](http://lamda.nju.edu.cn/CH.MainPage.ashx?AspxAutoDetectCookieSupport=1) 261 | * 苏剑林：[科学空间](https://spaces.ac.cn) 、[github](https://github.com/bojone) 262 | * 何恺明：[何恺明空间](http://kaiminghe.com/) 、[github](https://github.com/KaimingHe) 263 | * 华校专：[个人学习笔记](http://huaxiaozhuan.com/) 、[github](https://github.com/huaxz1986) 264 | * 黄海广：[知乎 ](https://www.zhihu.com/people/fengdu78/activities) 、[github](https://github.com/fengdu78) 265 | * 金陵书生：[知乎](https://www.zhihu.com/people/jlbookworm/activities) 266 | * 植物大佬：[github](https://github.com/plantsgo) 267 | * 蛇佬：[Snake](https://github.com/luoda888) 268 | * wepon：[github](https://github.com/wepe) 、[csdn](https://blog.csdn.net/u012162613) 269 | * Cortex Labs(比赛团队)：[Cortex ](https://github.com/CortexFoundation) 270 | * 杨培文：[主页](https://ypw.io/) 、[知乎](https://www.zhihu.com/people/yangpw/activities) 271 | * 莫烦：[github](https://github.com/MorvanZhou) 、[教学主页](https://morvanzhou.github.io/) 272 | * 徐亦达：[github](https://github.com/roboticcam) 、[bilibili](https://space.bilibili.com/327617676/#/) 、[优酷](http://i.youku.com/i/UMzIzNDgxNTg5Ng==?spm=a2h0j.11185381.module_basic_dayu_sub.DL~DT~A) 273 | * ApacheCN：[github](https://github.com/apachecn) 、[组织主页](http://www.apachecn.org/) 274 | * 机器之心：[知乎专栏](https://zhuanlan.zhihu.com/jiqizhixin) 、[主页](https://www.jiqizhixin.com/) 275 | 276 | ### Awesome系列 277 | 278 | --- 279 | 280 | * [资料汇总的汇总-zh](https://github.com/justjavac/awesome-awesomeness-zh_CN) 281 | * [资料汇总的汇总](https://github.com/bayandin/awesome-awesomeness) 282 | * [机器学习-zh](https://github.com/dadoubigege/awesome-machine-learning-cn) 283 | * [python](https://github.com/vinta/awesome-python) 284 | * [python-zh](https://github.com/jobbole/awesome-python-cn) 285 | * [tensorflow](https://github.com/jtoy/awesome-tensorflow) 286 | * [tensorflow-zh](https://github.com/fendouai/Awesome-TensorFlow-Chinese) 287 | * [面试](https://github.com/MaximAbramchuck/awesome-interview-questions) 288 | * [深度学习](https://github.com/ChristosChristofidis/awesome-deep-learning) 289 | * [Django](https://github.com/rosarior/awesome-django) 290 | * [RNN](https://github.com/kjw0612/awesome-rnn) 291 | * [计算机视觉](https://github.com/jbhuang0604/awesome-computer-vision) 292 | * [NLP](https://github.com/keon/awesome-nlp) 293 | * [NLP-zh](https://github.com/crownpku/Awesome-Chinese-NLP ) 294 | * [强化学习](https://github.com/aikorea/awesome-rl) 295 | * [算法](https://github.com/tayllan/awesome-algorithms) 296 | * [AI](https://github.com/owainlewis/awesome-artificial-intelligence) 297 | * [Python-时间序列](https://github.com/MaxBenChrist/awesome_time_series_in_python) 298 | * [project-ideas](https://github.com/NirantK/awesome-project-ideas) 299 | * [前端资源](https://github.com/helloqingfeng/Awsome-Front-End-learning-resource) 300 | * [docker](https://github.com/veggiemonk/awesome-docker) 301 | * [Pytorch-list](https://github.com/bharathgs/Awesome-pytorch-list) 302 | 303 | ### 知名实验室&研究所 304 | 305 | ---- 306 | 307 | * [腾讯AI Lab](https://ai.tencent.com/ailab/index.html) 308 | * [南大-Lambda机器学习与数据挖掘研究所](http://lamda.nju.edu.cn/CH.MainPage.ashx) 309 | * [DeepMind](https://deepmind.com/) 310 | * [达摩院实验室](https://damo.alibaba.com/labs/) 311 | * [MIT CSAI Lab](https://www.csail.mit.edu/) 312 | * [布里斯托大学智能系统实验室](https://intelligentsystems.bristol.ac.uk/) 313 | * [斯坦福视觉研究所](http://vision.stanford.edu/index.html) 314 | * [伯克利人工智能研究室](https://bair.berkeley.edu/) 315 | * [剑桥大学未来智能研究中心](http://www.lcfi.ac.uk/) 316 | 317 | 318 | 319 | ### GitHub gronization or Topic 320 | 321 | --- 322 | 323 | * [tensorflow](https://github.com/tensorflow) 324 | * [AI-ON](https://github.com/AI-ON) 325 | * [Awesome](https://github.com/topics/awesome) 326 | * [Google](https://github.com/google) 327 | * [Google-cloud](https://github.com/GoogleCloudPlatform) 328 | 329 | ### 数据集 330 | 331 | --- 332 | 333 | * [【NLP】funNLP中文NLP数据集](https://github.com/fighting41love/funNLP) 334 | * [百度点石数据集](http://dianshi.baidu.com/gemstone/datasets) 335 | * [kesci数据集](https://www.kesci.com/home/dataset) 336 | * [UCI机器学习数据集](http://archive.ics.uci.edu/ml/index.php) 337 | * [awesome数据集](https://github.com/awesomedata/awesome-public-datasets) 338 | * [【CV】Stanford行人视频](http://cvgl.stanford.edu/projects/uav_data/) 339 | * [数据堂](http://www.datatang.com/webfront/datatang_dataset.html) 340 | * [YaHoo数据集](https://webscope.sandbox.yahoo.com/?guccounter=2) 341 | * [【CV】人脸素描数据集](http://mmlab.ie.cuhk.edu.hk/archive/facesketch.html) 342 | * [【CV】宠物图片-分割数据集](http://www.robots.ox.ac.uk/~vgg/data/pets/) 343 | * [【CV】Berkeley图像分割数据集BSDS500](https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html) 344 | * [【CV】MIT 场景感知/解析/分割/多目标识别数据集](https://groups.csail.mit.edu/vision/datasets/ADE20K/) 345 | * [【GaTech】多模态二元行为数据集](http://www.cbi.gatech.edu/mmdb/) 346 | * [【CV】Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) 347 | * [【CV】大型Logo数据集](https://data.vision.ee.ethz.ch/sagea/lld/) 348 | * [【CV】动物属性标记数据集](http://cvml.ist.ac.at/AwA2/) 349 | * [【CV】日本漫画Manga109](https://dl.acm.org/citation.cfm?doid=3011549.3011551) 350 | * [【CV】俯拍街舞视频](http://homepages.inf.ed.ac.uk/rbf/CEILIDHDATA/) 351 | * [【CV】Pixiv(着色)图片数据集](https://github.com/jerryli27/pixiv_dataset) 352 | * [【CV】简笔画涂鸦数据集](https://github.com/googlecreativelab/quickdraw-dataset) 353 | * [【CV】服饰人像生成模型(&Chictopia10K[HumanParsing]时尚人像解析数据集](http://files.is.tue.mpg.de/classner/gp/) 354 | * [【CV】COCO像素级标注数据集](https://github.com/nightrome/cocostuff) 355 | * [【CV】大规模街道级图片(分割)数据集【Peter Kontschieder】](https://blog.mapillary.com/product/2017/05/03/mapillary-vistas-dataset.html) 356 | * [【CV】(街头)时尚服饰数据集(2000+标注图片)](https://github.com/bearpaw/clothing-co-parsing) 357 | * [数据科学/机器学习数据集汇总](https://elitedatascience.com/datasets) 358 | * [【CV】CORe50：连续目标识别数据集](https://vlomonaco.github.io/core50/) 359 | * [【CV】动作识别数据库](http://www.nada.kth.se/cvap/actions/) 360 | * [deepmind开源数据集](https://deepmind.com/research/open-source/) 361 | * [鸟叫声数据集【xeno-canto】](https://www.xeno-canto.org/) 362 | 363 | ### 书籍推荐 364 | 365 | --- 366 | 367 | [c-dafan](./books.md) 368 | 369 | ### 读研 370 | 371 | --- 372 | 373 | * [读研注意](./Postgraduate.md) 374 | 375 | -------------------------------------------------------------------------------- /books.md: -------------------------------------------------------------------------------- 1 | # 推荐书籍 2 | 3 | ## Python 4 | 5 | ### 基础 6 | 7 | * 流畅的Python 8 | * 等等 9 | 10 | ### 深化 11 | 12 | * python大战机器学习 13 | * 利用python进行数据分析 14 | * 流畅的Python 15 | * Python高级编程 16 | * python设计模式 17 | 18 | ## 深度学习 19 | 20 | ### 理论 21 | 22 | * 深度学习（人民邮电） 23 | * 等等 24 | 25 | ### 工具 26 | 27 | * tensorflow官网 28 | * keras官网 29 | 30 | ## 机器学习 31 | 32 | ### 理论 33 | 34 | * 统计学习方法 35 | * 机器学习（周志航） 36 | 37 | ### 实践 38 | 39 | * 机器学习实战 40 | * 等等 41 | 42 | ## 数据挖掘 43 | 44 | * python数据分析与数据挖掘 45 | * 数据挖掘 46 | 47 | ## 大数据 48 | 49 | ### Hadoop 50 | 51 | * Hadoop实战 52 | * Hadoop权威指南 53 | 54 | ### Spark 55 | 56 | * 官方文档 57 | * 等等 58 | 59 | ## 算法 60 | 61 | * 挑战程序设计大赛 62 | * 算法（红皮） 63 | 64 | 65 | -------------------------------------------------------------------------------- /papers.md: -------------------------------------------------------------------------------- 1 | # 论文推荐 2 | 3 | ## 图像 4 | 5 | ### 图像识别 6 | 7 | | 简称 | 全称 | 年 | 8 | | :----:| :--------------: | :------: | 9 | | [AlexNet][1] | [ImageNet Classification with Deep Convolutional Neural Networks][1] | 2012 | 10 | | [VGGNet][2] | [Very deep convolutional networks for large-scale image recognition][2] |2014| 11 | | [GoogLeNet][3] | [Going deeper with convolutions][3]|2014 12 | | [Batch Normalization][4] | [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift][4] |2015 13 | | [Inception V3][5] | [Rethinking the Inception Architecture for Computer Vision][5] | 2015 14 | | [ResNet][6] | [Deep Residual Learning for Image Recognition][6] |2015| 15 | | [ResNetXt][7] |[Aggregated Residual Transformations for Deep Neural Networks][7]|2017 16 | | [Xception][8] | [Xception: Deep Learning with Depthwise Separable Convolutions][8] | 2017 17 | | [Inception v4][9] | [Inception-ResNet and the Impact of Residual Connections on Learning][9] | 2017 18 | | [ShuffleNet][10] |[An Extremely Efficient Convolutional Neural Network for Mobile Devices][10] |2017| 19 | | [MobileNets][11] | [Efficient Convolutional Neural Networks for Mobile Vision Applications][11]|2017 20 | |[SqueezeNet][12]|[SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size][12]|2017 21 | 22 | [1]:https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf 23 | [2]:https://arxiv.org/abs/1409.1556 24 | [3]:https://arxiv.org/abs/1409.4842 25 | [4]:http://de.arxiv.org/pdf/1502.03167 26 | [5]:https://arxiv.org/abs/1512.00567 27 | [6]:https://arxiv.org/pdf/1512.03385.pdf 28 | [7]:https://arxiv.org/abs/1611.05431 29 | [8]:https://arxiv.org/abs/1610.02357 30 | [9]:https://arxiv.org/abs/1602.07261 31 | [10]:https://arxiv.org/abs/1707.01083 32 | [11]:https://arxiv.org/abs/1704.04861 33 | [12]:http://arxiv.org/abs/1602.07360 --------------------------------------------------------------------------------