ComputerScience
算法设计手册 豆瓣
The Algorithm Design Manual (2nd Ed.)
作者: 斯基恩纳 出版社: 清华大学出版社 2009 - 9
《算法设计手册(第2版)》是算法设计畅销书的最新版本,是设计实用且高效算法的最全面指导书。《算法设计手册(第2版)》揭密了算法的设计与分析,以简单易懂的写作风格,介绍了各种算法技术,着重强调了算法分析,全书包括两大部分,“技术”部分介绍了设计和分析计算机算法的各种方法,“资源”部分给出了大量的参考资源,以及算法实现的各种资源,此外,在作者的个人网址http://www.CS.sunysb.edu/~algorith/I-还提供了各种教学资源和参考材料,这些资源对读者很有参考价值。
《算法设计手册(第2版)》可以作为算法设计课程的主教材,也是程序人员、研究人员和学生的常备参考书。
The Ph.D. Grind 豆瓣 Goodreads
9.1 (45 个评分) 作者: Philip J·Guo 出版社: Clearway Logistics Phase 1a 2012 - 7
This book chronicles my six years of working towards a Ph.D. in Computer Science at Stanford University from 2006 to 2012. A diverse variety of people can benefit from reading it, including:
undergraduates who might be interested in pursuing a Ph.D.,
current Ph.D. students who are seeking guidance or inspiration,
professors who want to better understand Ph.D. students,
employers who hire and manage people with Ph.D. degrees,
professionals working in any creative or competitive field where self-driven initiative is crucial,
and educated adults (or precocious kids) who are curious about how academic research is produced.
The Fourth Paradigm 豆瓣 Goodreads 开放图书馆
作者: Tony Hey 出版社: Microsoft Research 2009 - 10 其它标题: The Fourth Paradigm: Data-Intensive Scientific Discovery / The fourth paradigm
This book presents the first broad look at the rapidly emerging field of data-intensive science, with the goal of influencing the worldwide scientific and computing research communities and inspiring the next generation of scientists. Increasingly, scientific breakthroughs will be powered by advanced computing capabilities that help researchers manipulate and explore massive datasets. The speed at which any given scientific discipline advances will depend on how well its researchers collaborate with one another, and with technologists, in areas of eScience such as databases, workflow management, visualization, and cloud-computing technologies. This collection of essays expands on the vision of pioneering computer scientist Jim Gray for a new, fourth paradigm of discovery based on data-intensive science and offers insights into how it can be fully realized.