一直以来CPU内部是绝大多数IT工程师难以触及的领域。纵使学习过计算机架构相关课程，自己动手实现CPU也始终遥不可及，因为这涉及计算机系统的最底层——芯片设计。而近年来FPGA芯片产品的发展与普及打破了这一阻碍，利用内部电路可重编程的FPGA，我们几乎可以实现任何逻辑电路，自然也包括CPU。
本书就是在这样一个背景下孕育而生的。本书利用FPGA，为读者开启了一个崭新的自制CPU的世界。全书分为3章，分别介绍计算机系统最底层的3个部分：CPU设计制作、电路板设计制造以及汇编编程。将如此广泛的技术内容以实践的方式融合成一册，该书可谓首屈一指。
本书可以帮助软件工程师深入了解硬件与底层，开发出高效代码。硬件工程师可以在本书基础上设计定制硬件，开发高速计算机系统。相信所有读者都可以在本书的阅读过程中，体会到自制计算机系统的乐趣与热情。

《高维信息几何与语音分析》共三个部分，第一部分是介绍语音分析的常见研究方法，第二部分是高维信息几何基础知识，它主要介绍了高维信息几何的欧氏空间与高维信息几何线性代数基础理论基本算法，第三部分给出了高维信息几何理论及其算法在语音分析中的实际应用，它主要是提出了高维信息几何点覆盖理论及几何分析方法，对连续语音在高维空间中的种种表现形式加以探讨，给出了语音信息映射到高维空间后的分布概况。

《中文信息处理丛书：统计自然语言处理（第2版）》全面介绍了统计自然语言处理的基本概念、理论方法和最新研究进展，内容包括形式语言与自动机及其在自然语言处理中的应用、语言模型、隐马尔可夫模型、语料库技术、汉语自动分词与词性标注、句法分析、词义消歧、篇章分析、统计机器翻译、语音翻译、文本分类、信息检索与问答系统、自动文摘和信息抽取、口语信息处理与人机对话系统等，既有对基础知识和理论模型的介绍，也有对相关问题的研究背景、实现方法和技术现状的详细阐述。
《中文信息处理丛书：统计自然语言处理（第2版）》可作为高等院校计算机、信息技术等相关专业的高年级本科生或研究生的教材或参考书，也可供从事自然语言处理、数据挖掘和人工智能等研究的相关人员参考。

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。

Thorough and accessible, this book presents the design principles of biological systems, and highlights the recurring circuit elements that make up biological networks. It provides a simple mathematical framework which can be used to understand and even design biological circuits. The text avoids specialist terms, focusing instead on several well-studied biological systems that concisely demonstrate key principles. "An Introduction to Systems Biology: Design Principles of Biological Circuits" builds a solid foundation for the intuitive understanding of general principles. It encourages the reader to ask why a system is designed in a particular way and then proceeds to answer with simplified models.

Optimization is an important tool used in decision science and for the analysis of physical systems used in engineering. One can trace its roots to the Calculus of Variations and the work of Euler and Lagrange. This natural and reasonable approach to mathematical programming covers numerical methods for finite-dimensional optimization problems. It begins with very simple ideas progressing through more complicated concepts, concentrating on methods for both unconstrained and constrained optimization.

"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.

《计算机病毒防范艺术》作者是赛门铁克（Symantec）公司安全响应中心首席安全架构师，他根据自己设计和改进Norton AntiVirus系统产品及培训病毒分析人员的过程中遇到的问题精心总结编写了本书。本书最大的特色是大胆深入地探讨了病毒知识的技术细节，从病毒的感染策略上深入分析病毒的复杂性，从文件、内存和网络等多个角度讨论病毒的感染技术，对过去20年来黑客们开发的各种病毒技巧进行了分类和讲解，并介绍了代码变形和其他新兴病毒感染技术，展示了当前计算机病毒和防毒软件最新技术，向读者传授计算机病毒分析和防护的方法学。

"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.

During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for "wide" data (p bigger than n), including multiple testing and false discovery rates.

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

《电子电气工程师必知必会(第2版)》从实际工作需要出发，总结了一名现代电子电气工程师在日常工作中最为关键的知识点，从简单的R、L、C元件，到复杂的运放、微处理器/微控制器、数模/模数转换器、电机、电源，再到元件的非理想性、电路的可靠性设计、仿真、焊接，以及电路和软件的故障处理等，文字生动幽默。此外，《电子电气工程师必知必会(第2版)》还以较大的篇幅介绍了作者作为研发部门的管理者，在人际沟通、管理等方面的心得体会。
《电子电气工程师必知必会(第2版)》既可供电气信息类专业的高校师生参考，也适合电气工程师阅读。

《支持向量机：理论、算法与拓展》以分类问题（模式识别、判别分析）和回归问题为背景，介绍支持向量机的基本理论、方法和应用。特别强调对所讨论的问题和处理方法的实质进行直观的解释和说明，因此具有很强的可读性。为使具有一般高等数学知识的读者能够顺利阅读，书中首先介绍了最优化的基础知识。《支持向量机：理论、算法与拓展》可作为理工类、管理学等专业的高年级本科生、研究生和教师的教材或教学参考书，也可供相关领域的科研人员和实际工作者阅读参考。

《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。

《组合数学(英文版)(第5版)》英文影印版由Pearson Education Asia Ltd．授权机械工业出版社独家出版。未经出版者书面许可，不得以任何方式复制或抄袭奉巾内容。仅限于中华人民共和国境内(不包括中国香港、澳门特别行政区和中同台湾地区)销售发行。《组合数学(英文版)(第5版)》封面贴有Pearson Education(培生教育出版集团)激光防伪标签，无标签者不得销售。English reprint edition copyright@2009 by Pearson Education Asia Limited and China Machine Press．
Original English language title：Introductory Combinatorics，Fifth Edition(ISBN978—0—1 3-602040-0)by Richard A．Brualdi，Copyright@2010，2004，1999，1992，1977 by Pearson Education，lnc． All rights reserved．
Published by arrangement with the original publisher，Pearson Education，Inc．publishing as Prentice Hall．
For sale and distribution in the People’S Republic of China exclusively(except Taiwan，Hung Kong SAR and Macau SAR)．

本书以经典理论与现代应用相结合的方式介绍了初等数论的基本概念和方法，内容包括整除、同余、二次剩余、原根以及整数的阶的讨论和计算。此外，书中附有60多位对数论有贡献的数学家的传略。
本书内容丰富，趣味性强，条理清晰，既可以作为高等院校计算机及相关专业的数论教材，也可以作为对数论和密码学感兴趣的读者的初级读物。
本书是数论课程的经典教材，自出版以来，深受读者好评，被美国加州大学伯克利分校，伊利诺伊大学，得克萨斯大学等数百所名校采用。
经典理论与现代应用的结合是本书的一大特色。第5版通过增强实例和练习，将数论的应用引入了更高的境界，同时更新并扩充了对密码学这一热点论题的讨论。与时俱进是本书的又一大特色，为使本版与最新的研究成果及近几年的新理论优美结合，作者花费了大量心血。本书还以别出心裁的习题安排而著名，书中收入的富于挑战性的习题旨在帮助读者探究数论中的关键概念，同时提供两类习题：一类是计算题；另一类是上机编程练习，这使得读者能够将数学理论与编程技巧实践联系起来。
目录
前言
符号表
何谓数论
第1章 整数
1.1　数和序列
1.2　和与积
1.3　数学归纳法
1.4　斐波那契数
1.5　整除性
第2章 整数的表示法和运算
2.1　整数的表示法
2.2　整数的计算机运算
2.3　整数运算的复杂度
第3章 素数和最大公因子
3.1　素数
3.2　素数的分布
3.3　最大公因子
3.4　欧几里得算法
3.5　算术基本定理
3.6　因子分解法和费马数
3.7　线性丢番图方程
第4章 同余
4.1　同余引言
4.2　线性同余方程
4.3　中国剩余定理
4.4　求解多项式同余方程
4.5　线性同余方程组
4.6　利用波拉德方法分解整数
第5章 同余的应用
5.1　整除性检验
5.2　万年历
5.3　循环赛赛程
5.4　散列函数
5.5　校验位
第6章 特殊的同余式
6.1　威尔逊定理和费马小定理
6.2　伪素数
6.3　欧拉定理
第7章 乘性函数
7.1　欧拉函数
7.2　因子和与因子个数
7.3　完全数和梅森素数
7.4　莫比乌斯反演
第8章 密码学
8.1　字符密码
8.2　分组密码和流密码
8.3　取幂密码
8.4　公钥密码
8.5　背包密码
8.6　密码协议及应用
第9章 原根
9.1　整数的阶和原根
9.2　素数的原根
9.3　原根的存在性
9.4　指数的算术
9.5　用整数的阶和原根进行素性检验
9.6　通用指数
第10章 原根与整数的阶的应用
10.1　伪随机数
10.2　埃尔伽莫密码系统
10.3 电话线缆绞接中的一个应用
第11章 二次剩余
11.1　二次剩余与二次非剩余
……
第12章　十进制分数与连分数
第13章　某些非线性丢番图方程
第14章　高斯整数
附录
参考文献

《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。

Git is the version control system developed by Linus Torvalds for Linux kernel development. It took the open source world by storm since its inception in 2005, and is used by small development shops and giants like Google, Red Hat, and IBM, and of course many open source projects.
* A book by Git experts to turn you into a Git expert
* Introduces the world of distributed version control
* Shows how to build a Git development workflow
What you’ll learn
* Use Git as a programmer or a project leader.
* Become a fluent Git user.
* Use distributed features of Git to the full.
* Acquire the ability to insert Git in the development workflow.
* Migrate programming projects from other SCMs to Git.
* Learn how to extend Git.
This book is for all open source developers: you are bound to encounter it somewhere in the course of your working life. Proprietary software developers will appreciate Git’s enormous scalability, since it is used for the Linux project, which comprises thousands of developers and testers.

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.