“Miao Fan”版本间的差异

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[https://godfanmiao.gitee.io/homepage/ 个人主页]
[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/b/bd/Miao_Fan_%282015_C.V.%29.pdf Curriculum Vitae]
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[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/d/dc/%E8%8C%83%E6%B7%BC-%E4%B8%AD%E6%96%87-%E4%B8%80%E9%A1%B5%E7%AE%80%E5%8E%86.pdf 个人简历]
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[http://weibo.com/fanmiaothu/ 新浪微博]
 
[http://weibo.com/fanmiaothu/ 新浪微博]
 
 
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[https://scholar.google.com/citations?user=aPlHReAAAAAJ&hl=en Google Scholar(谷歌学术档案)]
 
 
[http://dblp.uni-trier.de/pers/hd/f/Fan:Miao DBLP(DBLP论文索引)]
 
 
[http://www.kaggle.com/michaelfan Kaggle Profile(Kaggle竞赛成绩)]
 
 
 
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[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/a/a9/B.ENG_Dissertation_-Miao_Fan-.pdf B.Eng Dissertation (北京邮电大学本科优秀毕业设计:基于百科知识的中文自动问题生成技术的研究与系统实现)]
 
 
[https://onedrive.live.com/redir?resid=76645C25A8914A0B!8071&authkey=!AIYUeWmYlXPFWSU&ithint=file%2cpptx Ph.D. Research Proposal(清华大学博士开题报告:基于低维表示的大规模实体关系挖掘技术研究)]
 
 
[Ph.D. Thesis (清华大学博士论文)] [http://cslt.riit.tsinghua.edu.cn/mediawiki/images/f/f2/MF_PhD_thesis_references.pdf References(已发表的学术论文集)]
 
 
[http://cslt.riit.tsinghua.edu.cn/mediawiki/images/5/59/Special_talks_NYU--M.F.-.pdf Slides of NYU Special Talks(美国纽约大学授课讲义:Statistical NLP: A Machine Learning Perspective)]
 
 
 
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[https://union-click.jd.com/jdc?d=lKjrlR&come=appmessage 范淼、李超;《Python机器学习及实践:从零开始通往Kaggle竞赛之路》; 清华大学出版社 (Tsinghua University Press)] [http://www.tup.tsinghua.edu.cn/upload/books/yz/069392-01.pdf 书籍样章] [https://union-click.jd.com/jdc?d=lKjrlR&come=appmessage 京东购书] [http://pan.baidu.com/s/1bGp15G 源代码 (Source Codes)] [https://git.coding.net/fanmiao_thu/Python_ML_and_Kaggle.git Github]
 
 
[范淼;《PySpark 2.0 分布式机器学习与大数据分析》 ;清华大学出版社 (Tsinghua University Press) 计划出版] [https://www.youtube.com/playlist?list=PL-x35fyliRwhDv3g1dae8v2F6-_bzBfGK YouTube视频] [源代码(Source Codes)]
 
 
[范淼;《Python深度学习实践:探秘谷歌TensorFlow》] [https://www.tensorflow.org/ Google TensorFlow] [https://arxiv.org/pdf/1610.01178v1.pdf A Tour of TensorFlow]
 
 
[范淼;《Python推荐系统实战》(Recommender System in Practice with Python Programming)] [https://arxiv.org/pdf/1606.07792v1.pdf Wide & Deep Learning for Recommender Systems]
 
 
 
 
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[http://www.deeplearningbook.org/contents/acknowledgements.html Deep Learning(校对图书)] [http://www.deeplearningbook.org 公开下载网址] [http://deeplearning.stanford.edu/wiki/index.php/UFLDL_Tutorial Tutorial(教程)] [https://www.cs.toronto.edu/~hinton/absps/NatureDeepReview.pdf Yann LeCun, Yoshua Bengio & Geoffrey Hinton; Nature: Review of Deep Learning] 
 
 
[http://cs229.stanford.edu/materials.html  Machine Learning (Stanford University)] [https://www.youtube.com/playlist?list=PLC5F94EBABE15D569 YouTube(视频)] [http://open.163.com/special/opencourse/machinelearning.html 网易公开课视频]
 
 
[http://mmds.org/ Mining of Massive Datasets] [https://www.youtube.com/channel/UC_Oao2FYkLAUlUVkBfze4jg Youtube(视频)]
 
 
[http://spark.apache.org/docs/latest/api/python/index.html  PySpark 2.x] [https://www.youtube.com/playlist?list=PL-x35fyliRwhDv3g1dae8v2F6-_bzBfGK YouTube视频] [https://www.gitbook.com/book/jaceklaskowski/mastering-apache-spark/details 参考文档]
 
 
[https://webdocs.cs.ualberta.ca/~sutton/book/bookdraft2016sep.pdf Reinforcement Learning: An Introduction (MIT Press)]
 
 
[https://www.cs.cornell.edu/jeh/book2016June9.pdf Foundations of Data Science]
 
 
 
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Outstanding Friends: [https://sites.google.com/site/wenbinghuangshomepage/ Wenbing Huang] [http://www.yindawei.com/ Dawei Yin]
 

2022年1月20日 (四) 07:12的最后版本

MF PHOTO.jpg 个人主页 新浪微博