Mining of Massive Datasets 在线电子书 图书标签: 数据挖掘 计算机 机器学习 Data Coursera CS 数据分析 软件工程
发表于2024-07-03
Mining of Massive Datasets 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024
勉强一刷吧。到时配合斯坦福的课再过一遍~
评分bug非常之多, 还找不到地方提交, 读起来极度痛苦, 前看后忘, 也许里面的算法本质上就是这样, bottom line至少近15年最新的论文成果被这么串讲一下, 本科生也能看懂
评分下学期课程参考textbook,听说professor还不错,打算好好学一下这门课
评分下学期课程参考textbook,听说professor还不错,打算好好学一下这门课
评分内容不错,但作为技术向的书有些浮于表面。
Jure Leskovec is Assistant Professor of Computer Science at Stanford University. His research focuses on mining large social and information networks. Problems he investigates are motivated by large scale data, the Web and on-line media. This research has won several awards including a Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship, Okawa Foundation Fellowship, and numerous best paper awards. His research has also been featured in popular press outlets such as the New York Times, the Wall Street Journal, the Washington Post, MIT Technology Review, NBC, BBC, CBC and Wired. Leskovec has also authored the Stanford Network Analysis Platform (SNAP, http://snap.stanford.edu), a general purpose network analysis and graph mining library that easily scales to massive networks with hundreds of millions of nodes and billions of edges. You can follow him on Twitter at @jure.
Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.
本来是计划读英文版《Mining of Massive Datasets》的,但看到打折,而且译者在序言中信誓旦旦地说翻译的很用心,就买了中文的。结果读了第一章就读不下去了,中文表述太烂了,很多句子让人产生无限歧义,磕磕绊绊,叫人生厌。因此决定再次放弃这样的中文翻译书。
评分并非传统的”数据挖掘”教材,更像是,“数据挖掘”在互联网的应用场景,所遇到的问题(数据量大)和解决方案; 不过老实说,这本书挺不好懂的。 大概 get 了几个不错的思想: 思想-1:务必充分利用数据的”稀疏性”,如数据充分稀疏时,可以利用 HASH 将数据“聚合”成“有效...
评分 评分看有同学说是 stanford的入门课程,按理说应该不是太难。作为初学者来说,本书翻译的实在不敢恭维,看了50多页是一头雾水,很多话实在是晦涩难懂。本书作用入门级课程来说,基本上涵盖了数据挖掘的各个大类,如果想细致研究某个领域的大拿就不用看了
评分这本书其实挺好的,但是真得看英文版。 这是我们上课的参考书之一,英文版有的地方没看懂,就打算找个中文版来看。看了中文版发现,这个翻译的水平基本是跟我大四,研一给老师翻译文章的水平一样的,可以看出这本书应该是找学生翻译的,而且是对专业领域还了解不深的学生翻译的...
Mining of Massive Datasets 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024