Mining of Massive Datasets 在線電子書 圖書標籤: 數據挖掘 計算機 機器學習 Data Coursera CS 數據分析 軟件工程
發表於2024-06-29
Mining of Massive Datasets 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024
下學期課程參考textbook,聽說professor還不錯,打算好好學一下這門課
評分行文很流暢,看到下麵很多人說翻譯的問題,由此推薦原版。配閤網課還是挺淺顯的,例子舉得也挺多,自學也可以。步驟寫的也很細,有條件完全可以照著碼,不晦澀,小白很喜歡。
評分下學期課程參考textbook,聽說professor還不錯,打算好好學一下這門課
評分內容不錯,但作為技術嚮的書有些浮於錶麵。
評分下學期課程參考textbook,聽說professor還不錯,打算好好學一下這門課
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.
麻烦支那猪以后翻译外文书籍,先找个稍微懂行的把书看一遍行吗! 鉴于中文翻译缩水不准的情况,本掉千辛万苦找来英文原版,一看到目录,本屌就硬了,尼玛作者太牛逼了! 最新补充一句,话说如果这本书的名字叫做类似《数据挖掘基础》的话,本屌绝壁不喷它。本来就是基础的基...
評分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。
評分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。
評分并非传统的”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...
評分内容是算法分析应该有的套路, 对于Correctness, Running Time, Storage的证明; 讲得很细, 一个星期要讲3个算法, 看懂以后全部忘光大概率要发生. 要是能多给些直觉解释就好了. Ullman的表达绝对是有问题的, 谁不承认谁就是不客观, 常常一句话我要琢磨2个小时, 比如DGIM算法有一...
Mining of Massive Datasets 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024