Mining of Massive Datasets pdf epub mobi txt 电子书 下载 2025
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.
- 数据挖掘
- 计算机
- 机器学习
- Data
- Coursera
- CS
- 数据分析
- 软件工程
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.
具体描述
读后感
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...
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...
评分用户评价
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
评分行文很流畅，看到下面很多人说翻译的问题，由此推荐原版。配合网课还是挺浅显的，例子举得也挺多，自学也可以。步骤写的也很细，有条件完全可以照着码，不晦涩，小白很喜欢。
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
评分行文很流畅，看到下面很多人说翻译的问题，由此推荐原版。配合网课还是挺浅显的，例子举得也挺多，自学也可以。步骤写的也很细，有条件完全可以照着码，不晦涩，小白很喜欢。
评分内容不错，但作为技术向的书有些浮于表面。
相关图书
本站所有内容均为互联网搜索引擎提供的公开搜索信息，本站不存储任何数据与内容，任何内容与数据均与本站无关，如有需要请联系相关搜索引擎包括但不限于百度，google,bing,sogou 等
© 2025 qciss.net All Rights Reserved. 小哈图书下载中心 版权所有