Programming Language Pragmatics is the most comprehensive programming language textbook available today. Taking the perspective that language design and language implementation are tightly interconnected, and that neither can be fully understood in isolation, this critically acclaimed and bestselling book has been thoroughly updated to cover the most recent developments in programming language design. With a new chapter on run-time program management and expanded coverage of concurrency, this new edition provides both students and professionals alike with a solid understanding of the most important issues driving software development today.

Classic programming foundations text now updated to familiarize students with the languages they are most likely to encounter in the workforce, including including Java 7, C++, C# 3.0, F#, Fortran 2008, Ada 2005, Scheme R6RS, and Perl 6.
New and expanded coverage of concurrency and run-time systems ensures students and professionals understand the most important advances driving software today.
Includes over 800 numbered examples to help the reader quickly cross-reference and access content.

In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years.

本书以哲学、进化生物学、认知科学、逻辑学、计算机科学与自动机理论的结合点为轴心，以计算机科学哲学领域的计算主义和反计算主义相对立的研究立场为主线，试图在计算机革命或哲学的信息转向的背景下对人机类比、计算仿真与生命模拟的历史、现状和未来作深入浅出的评述，并着重探讨逻辑机器哲学和人工生命理论的发展、演变、研究现状，展示了计算机科学哲学理论发展的脉络、面临的挑战和困境以及解决问题的可能出路和走向。

Written by noted quantum computing theorist Scott Aaronson, this book takes readers on a tour through some of the deepest ideas of maths, computer science and physics. Full of insights, arguments and philosophical perspectives, the book covers an amazing array of topics. Beginning in antiquity with Democritus, it progresses through logic and set theory, computability and complexity theory, quantum computing, cryptography, the information content of quantum states and the interpretation of quantum mechanics. There are also extended discussions about time travel, Newcomb's Paradox, the anthropic principle and the views of Roger Penrose. Aaronson's informal style makes this fascinating book accessible to readers with scientific backgrounds, as well as students and researchers working in physics, computer science, mathematics and philosophy.

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects.

Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

This book comprehensively discusses the neural network models from a statistical mechanics perspective. It starts with one of the most influential developments in the theory of neural networks: Hopfield's analysis of networks with symmetric connections using the spin system approach and using the notion of an energy function from physics. Introduction to the Theory of Neural Computation uses these powerful tools to analyze neural networks as associative memory stores and solvers of optimization problems. A detailed analysis of multi-layer networks and recurrent networks follow. The book ends with chapters on unsupervised learning and a formal treatment of the relationship between statistical mechanics and neural networks. Little information is provided about applications and implementations, and the treatment of the material reflects the background of the authors as physicists. However the book is essential for a solid understanding of the computational potential of neural networks. Introduction to the Theory of Neural Computation assumes that the reader is familiar with undergraduate level mathematics, but does not have any background in physics. All of the necessary tools are introduced in the book.

《椭圆曲线及其在密码学中的应用:导引》以介绍椭圆曲线在密码学中的应用为目标，用浅显易懂的语言全面讲述了椭圆曲线公钥密码的相关知识，包括公钥密码学概述、有限域上椭圆曲线的算术理论、椭圆曲线上离散对数的求解算法以及有限域上椭圆曲线的求解算法等。

支持向量机是数据挖掘中的一个新方法。支持向量机能非常成功地处理回归问题(时间序列分析)和模式识别(分类问题、判别分析)等诸多问题，并可推广于预测和综合评价等领域，因此可应用于理科、工科和管理等多种学科。目前国际上支持向量机在理论研究和实际应用两方面都正处于飞速发展阶段。希望本书能促进它在我国的普及与提高。
本书对象既包括关心理论的研究工作者，也包括关心应用的实际工作者。对于有关领域的具有高等数学知识的实际工作者，略去书中的某些理论部分，仍能对支持向量机的本质有一个概括的理解，从而用它解决自己的问题。
本书适合高等院校高年级学生、研究生、教师和相关科研人员及相关领域的实际工作者使用。

《计算机代数系统的数学原理》主要介绍了计算机代数系统的数学理论、经典结果和著名算法。全书包含高精度运算、数论、数学常数、精确线性代数、多项式、方程求解、符号极限、符号求和、符号积分、微分方程符号解等10个部分，涵盖了构建计算机代数系统的最基础也是最重要的内容。书中的许多内容是第一次被系统地整理后出现在中文文献中，并在一些领域体现了本方向的最新进展。

《汇编语言(第2版)》是各种CPU提供的机器指令的助记符的集合，人们可以用汇编语言直接控制硬件系统进行工作。汇编语言是很多相关课程(如数据结构、操作系统、微机原理等)的重要基础。为了更好地引导、帮助读者学习汇编语言，作者以循序渐进的思想精心创作了《汇编语言(第2版)》。《汇编语言(第2版)》具有如下特点：采用了全新的结构对课程的内容进行组织，对知识进行最小化分割，为读者构造了循序渐进的学习线索；在深入本质的层面上对汇编语言进行讲解；对关键环节进行深入的剖析。

从程序员的视角，看计算机系统！
本书适用于那些想要写出更快、更可靠程序的程序员。通过掌握程序是如何映射到系统上，以及程序是如何执行的，读者能够更好的理解程序的行为为什么是这样的，以及效率低下是如何造成的。粗略来看，计算机系统包括处理器和存储器硬件、编译器、操作系统和网络互连环境。而通过程序员的视角，读者可以清晰地明白学习计算机系统的内部工作原理会对他们今后作为计算机科学研究者和工程师的工作有进一步的帮助。它还有助于为进一步学习计算机体系结构、操作系统、编译器和网络互连做好准备。
本书的主要论题包括：数据表示、C程序的机器级表示、处理器结构，程序优化、存储器层次结构、链接、异常控制流、虚拟存储器和存储器管理、系统级I/O、网络编程和并发编程。书中所覆盖的内容主要是这些方面是如何影响应用和系统程序员的。例如，在讲述数据表示时，本书说明了用来表示数字的表示方法是有限的，它能够近似地表示整数和实数，但是这种表示方法是有限制的，程序员必须了解。在讲述高速缓存时，本书讨论了矩阵代码中的循环变量的顺序是如何影响程序的性能的。在讨论网络互连时，本书描述了并发服务器如何能有效地处理来自多个客户端的请求。
本书基于Intel兼容（IA32）机器，在Unix或者相关的操作系统（例如，Linux）上执行C程序。虽然书中包括了一些帮助读者将Java转化成C的提示，但是还是要求读者对C或者C++有一定的了解。
您可以通过本书的Web网站www.csapp.cs.cmu.edu获得完整的资料，包括实验和作业，授课笔记和代码示例。
本书英文版久负盛名，被众多专业人士称为“最伟大的计算机教材”之一，著名的美国卡内基梅隆大学计算机科学系一直将本书作为教材使用，程序员眼中的透彻讲述计算机系统的扛鼎之作。作者Randal E. Bryant是卡耐基梅隆大学的计算机科学系主任，ACM和IEEE双院士（Fellow），其研究成果多次获得ACM和IEEE颁发的大奖。
本书共分十三章，分别介绍了信息的表示和处理、程序的机器级表示、处理器体系结构、存储器层次结构、静态和动态链接、虚拟存储器、系统级I/O、网络编程和并发编程等精彩内容。其目的是解释计算机系统的所有本质概念，并向读者展示这些概念是如何实际地影响应用程序的正确性、性能和实用性。与其他主要针对系统构造人员的系统类书籍不同，这本书是写给程序员的，是从程序员的角度来描述的。本书为软件和硬件之间搭起了一个桥梁，它给出了一种帮助读者分别从硬件和软件的角度去理解一个程序及其行为的途径，这也填补了国内计算机系统教学中的一个空白。本书的最大优点是帮助读者理解概念，让读者很清楚地在脑海中构造一个层次型的计算机系统，从最低层数据在内存中的表示（如我们一直陌生的浮点数表示），到流水线指令的构成，到虚拟存储器，到编译系统，到动态加载库，到最后的用户应用。
本书提供了大量的例子和练习及部分答案。尤其值得一提的是，对于每一个基本概念都有相应的笔头或程序试验，加深读者的理解。

从计算机发展史早期的Cobol、Fortran到后来的C、Java，编程语言的家族不断壮大。除了这些广为人知的语言外，还涌现了Erlang、Ruby等后起之秀，它们虽被喻为小众语言，但因其独特性也吸引了为数不少的追随者。
Bruce A. Tate是软件行业的一名老兵，他有一个宏伟目标：用一本书的篇幅切中要害地探索七种不同的语言。本书就是他的成果。书中介绍了Ruby、Io、Prolog、Scala、Erlang、Clojure和Haskell这七种语言，关注每一门语言的精髓和特性，重点解决如下问题：这门语言的类型模型是什么，编程范式是什么，如何与其交互，有哪些决策构造和核心数据结构，有哪些独特的核心特性。
在这个飞速发展的信息时代，程序员仅仅掌握甚至精通一门语言是远远不够的。了解多门语言蕴涵的思维方式，在编码中互相借鉴，再挑出一两门对自己口味的语言深入学习，这些已经成为在软件行业中安身立命之本。从这个意义上说，每个程序员都应该看看这本书。

网络科学作为一门新兴的学科越来越引入瞩目。网络科学能帮助读者设计更快、更有弹性的通信网络；能用于调整电力网络、电信网络和飞行航线等基础设施系统；可以为市场动态建模；能帮助理解生物系统中的同步；能用于分析人们之间的社会互动……
这是第一本全面审视新出现的网络科学的论著。书中研究了各种网络——规则网络、随机网络、小世界网络、影响网络、无标度网络和社会网络等，并将网络过程和行为应用于涌现、传染病、同步和风险方面。本书的独特之处在于将跨计算机科学、生物学、物理学、社会网络分析、经济学和市场营销等多学科的专业概念整合了起来。
本书为网络科学领域提供了全新的理解和阐释，是研究人员、专业人员以及工程、计算、生物领域的技术人员不可缺少的参考资料，也可以作为相关领域研究的高年级和研究生教材。

The purpose of this book is to help you understand how to program shared-memory parallel machines without risking your sanity.1 By describing the algorithms and designs that have worked well in the past, we hope to help you avoid at least some of the pitfalls that have beset parallel projects. But you should think of this book as a foundation on which to build, rather than as a completed cathedral. Your mission, if you choose to accept, is to help make further progress in the exciting field of parallel programming, progress that should in time render this book obsolete. Parallel programming is not as hard as it is reputed, and it is hoped that this book makes it even
easier for you.

Tool-making or culture, language or religious belief: ever since Darwin, thinkers have struggled to identify what fundamentally differentiates human beings from other animals. In this much-anticipated book, Michael Tomasello weaves his twenty years of comparative studies of humans and great apes into a compelling argument that cooperative social interaction is the key to our cognitive uniqueness. Once our ancestors learned to put their heads together with others to pursue shared goals, humankind was on an evolutionary path all its own.

Tomasello argues that our prehuman ancestors, like today's great apes, were social beings who could solve problems by thinking. But they were almost entirely competitive, aiming only at their individual goals. As ecological changes forced them into more cooperative living arrangements, early humans had to coordinate their actions and communicate their thoughts with collaborative partners. Tomasello's "shared intentionality hypothesis" captures how these more socially complex forms of life led to more conceptually complex forms of thinking. In order to survive, humans had to learn to see the world from multiple social perspectives, to draw socially recursive inferences, and to monitor their own thinking via the normative standards of the group. Even language and culture arose from the preexisting need to work together. What differentiates us most from other great apes, Tomasello proposes, are the new forms of thinking engendered by our new forms of collaborative and communicative interaction.

A Natural History of Human Thinking is the most detailed scientific analysis to date of the connection between human sociality and cognition.

This well-accepted introduction to computational geometry is a textbook for high-level undergraduate and low-level graduate courses. The focus is on algorithms and hence the book is well suited for students in computer science and engineering. Motivation is provided from the application areas: all solutions and techniques from computational geometry are related to particular applications in robotics, graphics, CAD/CAM, and geographic information systems. For students this motivation will be especially welcome. Modern insights in computational geometry are used to provide solutions that are both efficient and easy to understand and implement. All the basic techniques and topics from computational geometry, as well as several more advanced topics, are covered. The book is largely self-contained and can be used for self-study by anyone with a basic background in algorithms. In this third edition, besides revisions to the second edition, new sections discussing Voronoi diagrams of line segments, farthest-point Voronoi diagrams, and realistic input models have been added.