Difference between revisions of "Miao Fan"

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[https://scholar.google.com/citations?user=aPlHReAAAAAJ&hl=en Google Scholar(谷歌学术档案)]
 
[https://scholar.google.com/citations?user=aPlHReAAAAAJ&hl=en Google Scholar(谷歌学术档案)]
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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)]
 
[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)]
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[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://pan.baidu.com/s/1bGp15G 源代码 (Source Codes)]  [http://pan.baidu.com/s/1bGp15G Slides]      [https://git.coding.net/fanmiao_thu/Python_ML_and_Kaggle.git Github]
 
[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://pan.baidu.com/s/1bGp15G 源代码 (Source Codes)]  [http://pan.baidu.com/s/1bGp15G Slides]      [https://git.coding.net/fanmiao_thu/Python_ML_and_Kaggle.git Github]
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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://www.deeplearningbook.org/contents/acknowledgements.html Deep Learning(校对图书)] [http://www.deeplearningbook.org 公开下载网址]

Revision as of 05:48, 2 August 2016

Curriculum Vitae 个人简历


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Google Scholar(谷歌学术档案)

DBLP(DBLP论文索引)

Kaggle Profile(Kaggle竞赛成绩)


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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)

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)]

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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[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)] 源代码 (Source Codes) Slides Github

[范淼;《PySpark分布式机器学习与大数据分析》 (Distributed Machine Learning and Big Data Analysis with PySpark);清华大学出版社] YouTube视频 [源代码(Source Codes)]

[范淼;《Python推荐系统实践》(Recommender System in Practice with Python Programming)]


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Deep Learning(校对图书) 公开下载网址

Machine Learning (Stanford University) Youtube(视频)

Mining of Massive Datasets