This text, extensively class-tested over a decade at UC Berkeley and UC San Diego, explains the fundamentals of algorithms in a story line that makes the material enjoyable and easy to digest. Emphasis is placed on understanding the crisp mathematical idea behind each algorithm, in a manner that is intuitive and rigorous without being unduly formal.

This beginning graduate textbook describes both recent achievements and classical results of computational complexity theory. Requiring essentially no background apart from mathematical maturity, the book can be used as a reference for self-study for anyone interested in complexity, including physicists, mathematicians, and other scientists, as well as a textbook for a variety of courses and seminars. More than 300 exercises are included with a selected hint set.
Contents
Part I. Basic Complexity Classes: 1. The computational model - and why it doesn’t matter; 2. NP and NP completeness; 3. Diagonalization; 4. Space complexity; 5. The polynomial hierarchy and alternations; 6. Boolean circuits; 7. Randomized computation; 8. Interactive proofs; 9. Cryptography; 10. Quantum computation; 11. PCP theorem and hardness of approximation: an introduction; Part II. Lower Bounds for Concrete Computational Models: 12. Decision trees; 13. Communication complexity; 14. Circuit lower bounds; 15. Proof complexity; 16. Algebraic computation models; Part III. Advanced Topics: 17. Complexity of counting; 18. Average case complexity: Levin’s theory; 19. Hardness amplification and error correcting codes; 20. Derandomization; 21. Pseudorandom constructions: expanders and extractors; 22. Proofs of PCP theorems and the Fourier transform technique; 23. Why are circuit lower bounds so difficult?; Appendix A: mathematical background.
Reviews
Pre-Publication Review: "This text is a major achievement that brings together all of the important developments in complexity theory. Student and researchers alike will find it to be an immensely useful resource."
Michael Sipser, MIT, author of Introduction to the Theory of Computation
Pre-Publication Review: "Computational complexity theory is at the core of theoretical computer science research. This book contains essentially all of the (many) exciting developments of the last two decades, with high level intuition and detailed technical proofs. It is a must for everyone interested in this field."
Avi Wigderson, Professor, Institute for Advanced Study, Princeton
Pre-Publication Review: "This book by two leading theoretical computer scientists provides a comprehensive,insightful and mathematically precise overview of computational complexity theory, ranging from early foundational work to emerging areas such as quantum computation and hardness of approximation. It will serve the needs of a wide audience, ranging from experienced researchers to graduate students and ambitious undergraduates seeking an introduction to the mathematical foundations of computer science. I will keep it at my side as a useful reference for my own teaching and research."
Richard M. Karp, University Professor, University of California at Berkeley

For many applications, a randomized algorithm is either the simplest or the fastest algorithm available, and sometimes both. This book introduces the basic concepts in the design and analysis of randomized algorithms. The first part of the text presents basic tools such as probability theory and probabilistic analysis that are frequently used in algorithmic applications. Algorithmic examples are also given to illustrate the use of each tool in a concrete setting. In the second part of the book, each chapter focuses on an important area to which randomized algorithms can be applied, providing a comprehensive and representative selection of the algorithms that might be used in each of these areas. Although written primarily as a text for advanced undergraduates and graduate students, this book should also prove invaluable as a reference for professionals and researchers.

《范畴论》作者在书中使用的是现代范畴论通用的概念和术语，但是在对一些基本概念和理论的处理过程中，作者尝试使用比较简洁直接的方法，避免烦琐的论述。《范畴论》的前3章是范畴论的基础内容，适合高年级本科生和研究生的教学以及科研人员对范畴论基础知识的需要，第4章可供从事代数拓扑学尤其是同调代数研究的研究生和科研人员学习和参考，第5章既可以为从事代数几何的科研人员参考，同时也可为希望进一步学习Topos理论的读者提供层论方面的预备知识。

本书是第一本全面阐述如何构建健壮和准确的多语自然语言处理系统的图书，由两位资深专家编辑，集合了该领域众多尖端进展以及从广泛的研究和产业实践中总结出的实用解决方案。第一部分介绍现代自然语言处理的核心概念和理论基础，展示了如何理解单词和文档结构、分析语法、建模语言、识别蕴涵和检测冗余。第二部分彻底阐述与构建真实应用有关的实际考量，包括信息抽取、机器翻译、信息检索、文摘、问答、提炼、处理流水线等。

本书是计算机类专业操作系统课程的一本经典教材，自第一版问世以来，经历了近20年的锤炼，被认为是该课程教材的一本圣经。它对操作系统的概念和基本原理给出了清晰的阐述。本书所涉及的基本概念和算法均基于当前商用操作系统，并在非特定操作系统的通用环境中展开讲解。书中介绍了大量与流行操作系统相关的实现技术，包括Solaris 2、Linux、Windows NT、Windows 2000、OS/2和Apple Macintosh操作系统。此版包括了线程、Windows 2000的新章节，并新增了客户/服务器模型和网络文件系统、嵌入式操作系统、实时操作系统、分布式操作系统等。
作者Abraham Silerschatz是贝尔实验室信息科学研究中心的副主任， Greg Gagne是威斯敏斯特学院计算机学系主任，Peter Baer Galvin曾在布朗大学计算机科学系执教，现为Corporate Technologies公司的首席技术专家。

本书是一本在大学中广泛使用的经典数学教科书。书中讲解了许多计算机科学中用到的数学知识和技巧，教你如何把一个实际问题一步步演化为数学模型，然后通过计算机解决它，特别着墨于算法分析方面，其主要内容涉及和式、整值函数、数论、二项式系数、特殊的数、生成函数、离散概率、渐近式等，都是编程所必备的知识。
书中不仅讲述了数学问题和技巧，而且教导解决问题的方法，解说深入浅出，妙趣横生。大师们诙谐、细腻的笔触，描绘着数学工作中的欢乐和忧伤，那些或平淡、或深刻、或严肃、或幽默的涂鸦，更让我们在轻松愉悦的心境下体会数学的美妙。
本书面向从事计算机科学、计算数学、计算技术诸方面工作的人员，以及高等院校相关专业的师生。

本书深入浅出地阐述了计算机科学中许多基本而重要的概念，包括布尔逻辑、有限自动机、编程语言、图灵机的普遍性、信息论、算法、并行计算、量子计算、神经网络、机器学习乃至自组织系统。
作者高屋建瓴式的概括，既不失深度，又妙趣横生，相信读者读后会有很多启发。
目录：
序言：石的奇迹
第一章　通用件
第二章　万能积木
第三章　程序设计
第四章　图灵机的普适性
第五章　算法和探索法
第六章　存储：信息与密码
第七章　速度：并行计算机
第八章 自学习与自适应的计算机
第九章　跨越工程设计
致谢

Thirty-eight chapters, comissioned from experts all over the world, describe major concepts, methods, and applications in computational linguistics. Part I, Linguistic Fundamentals, provides an overview of the field suitable for senior undergraduates and non-specialists from other fields of linguistics and related disciplines. Part II describes current tasks, techniques, and tools in Natural Language Processing and aims to meet the needs of post-doctoral workers and others embarking on computational language research. Part III surveys current applications. This book is a state-of-the-art reference to one of the most active and productive fields in linguistics. It will be of interest and practical use to a wide range of linguists, as well as to researchers in such fields as informatics, artificial intelligence, language engineering, and cognitive science.

本书是关于形式语言、自动机理论和计算复杂性方面的经典教材，是三位理论计算大师的巅峰之作，现已更新到第3版。书中涵盖了有穷自动机、正则表达式与语言、正则语言的性质、上下文无关文法及上下文无关语言、下推自动机、上下文无关语言的，陸质、图灵机、不可判定性以及难解问题等内容。
本书已被世界许多著名大学采用为计算机理论课程的教材或教学参考书，适合用作国内高校计算机专业高年级本科生或研究生的教材，还可供从事理论计算工作的研究人员参考。

"Introduction to Computing Systems: From bits & gates to C & beyond", now in its second edition, is designed to give students a better understanding of computing early in their college careers in order to give them a stronger foundation for later courses. The book is in two parts: the underlying structure of a computer, and programming in a high level language and programming methodology. To understand the computer, the authors introduce the LC-3 and provide the LC-3 Simulator to give students hands-on access for testing what they learn. To develop their understanding of programming and programming methodology, they use the C programming language.The book takes a "motivated" bottom-up approach, where the students first get exposed to the big picture and then start at the bottom and build their knowledge bottom-up. Within each smaller unit, the same motivated bottom-up approach is followed. Every step of the way, students learn new things, building on what they already know. The authors feel that this approach encourages deeper understanding and downplays the need for memorizing. Students develop a greater breadth of understanding, since they see how the various parts of the computer fit together.

"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, co-chair of OpenAI; co-founder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Essential Information about Algorithms and Data Structures A Classic Reference The latest version of Sedgewick,s best-selling series, reflecting an indispensable body of knowledge developed over the past several decades. Broad Coverage Full treatment of data structures and algorithms for sorting, searching, graph processing, and string processing, including fifty algorithms every programmer should know. See

Eugene Charniak breaks new ground in artificial intelligenceresearch by presenting statistical language processing from an artificial intelligence point of view in a text for researchers and scientists with a traditional computer science background.New, exacting empirical methods are needed to break the deadlock in such areas of artificial intelligence as robotics, knowledge representation, machine learning, machine translation, and natural language processing (NLP). It is time, Charniak observes, to switch paradigms. This text introduces statistical language processing techniques;word tagging, parsing with probabilistic context free grammars, grammar induction, syntactic disambiguation, semantic wordclasses, word-sense disambiguation;along with the underlying mathematics and chapter exercises.Charniak points out that as a method of attacking NLP problems, the statistical approach has several advantages. It is grounded in real text and therefore promises to produce usable results, and it offers an obvious way to approach learning: "one simply gathers statistics."Language, Speech, and Communication

《组合数学》(原书第4版)侧重于组合数学的概念和思想，包括鸽巢原理、计数技术、排列组合、Polya计数法、二项式系数、容斥原理、生成函数和递推关系以及组合结构(匹配、实验设计、图)等，深入浅出地表达了作者对该领域全面和深刻的理解，介绍了历史上源于数学游戏和娱乐的大量实例，其中对Polya计数、Burnside定理等的完美处理使得不熟悉群论的学生也能够读懂。除包含第3版中的内容外，本版又进行了更新，增加了莫比乌斯反演(作为容斥原理的推广)、格路径、Schroder数等内容。此外，各章均包含大量练习题，并在书末给出了参考答案与提示。

《数值分析》以收敛性、复杂性、条件作用、压缩和正交性这5个主要思想为核心进行展开。内容包括求解方程组、插值、最小二乘、数值微分、数值积分、微分方程及边值问题、随机数及其应用、三角插值、压缩、最优化等。每章都有一个实例检验，有助于读者了解到相关应用领域。附录中介绍了矩阵代数和MATLAB，并提供了部分习题的答案。
《数值分析》内容广泛，实例丰富，可作为自然科学、工程技术、计算机科学、数学、金融等专业人员进行教学和研究的参考书。

This course is offered to undergraduates and is an elementary discrete mathematics course oriented towards applications in computer science and engineering. Topics covered include: formal logic notation, induction, sets and relations, permutations and combinations, counting principles, and discrete probability.