"This is an eminently readable book which an ordinary programmer, unskilled in mathematical analysis and wary of theoretical algorithms, ought to be able to pick up and get a lot out of.." - Steve Summit, author of C Programming FAQs Sedgewick has a real gift for explaining concepts in a way that makes them easy to understand. The use of real programs in page-size (or less) chunks that can be easily understood is a real plus. The figures, programs, and tables are a significant contribution to the learning experience of the reader; they make this book distinctive. - William A. Ward, University of South Alabama Robert Sedgewick has thoroughly rewritten and substantially expanded his popular work to provide current and comprehensive coverage of important algorithms and data structures. Many new algorithms are presented, and the explanations of each algorithm are much more detailed than in previous editions. A new text design and detailed, innovative figures, with accompanying commentary, greatly enhance the presentation. The third edition retains the successful blend of theory and practice that has made Sedgewick's work an invaluable resource for more than 250,000 programmers! This particular book, Parts 1-4, represents the essential first half of Sedgewick's complete work. It provides extensive coverage of fundamental data structures and algorithms for sorting, searching, and related applications. The algorithms and data structures are expressed in concise implementations in C, so that you can both appreciate their fundamental properties and test them on real applications. Of course, the substance of the book applies to programming in any language. Highlights * Expanded coverage of arrays, linked lists, strings, trees, and other basic data structures * Greater emphasis on abstract data types (ADTs) than in previous editions * Over 100 algorithms for sorting, selection, priority queue ADT implementations, and symbol table ADT (searching) implementations * New implementations of binomial queues, multiway radix sorting, Batcher's sorting networks, randomized BSTs, splay trees, skip lists, multiway tries, and much more * Increased quantitative information about the algorithms, including extensive empirical studies and basic analytic studies, giving you a basis for comparing them * Over 1000 new exercises to help you learn the properties of algorithms Whether you are a student learning the algorithms for the first time or a professional interested in having up-to-date reference material, you will find a wealth of useful information in this book.

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more; algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods; performance improvement techniques that work by transforming the input or output; and, downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface.

Computer Vision is fast becoming an important technology and is used in Mars robots, national security systems, automated factories, driver-less cars, and medical image analysis to new forms of human-computer interaction. OpenCV is the most common library for computer vision, providing hundreds of complex and fast algorithms. But it has a steep learning curve and limited in-depth tutorials. Mastering OpenCV with Practical Computer Vision Projects is the perfect book for developers with just basic OpenCV skills who want to try practical computer vision projects, as well as the seasoned OpenCV experts who want to add more Computer Vision topics to their skill set or gain more experience with OpenCV’s new C++ interface before migrating from the C API to the C++ API. Each chapter is a separate project including the necessary background knowledge, so try them all one-by-one or jump straight to the projects you’re most interested in. Create working prototypes from this book including real-time mobile apps, Augmented Reality, 3D shape from video, or track faces & eyes, fluid wall using Kinect, number plate recognition and so on. Mastering OpenCV with Practical Computer Vision Projects gives you rapid training in nine computer vision areas with useful projects. - Allows anyone with basic OpenCV experience to rapidly obtain skills in many computer vision topics, for research or commercial use - Each chapter is a separate project covering a computer vision problem, written by a professional with proven experience on that topic - All projects include a step-by-step tutorial and full source-code, using the C++ interface of OpenCV

Create and publish your own interactive data visualization projects on the Web, even if you have no experience with either web development or data visualization. It's easy with this hands-on guide. You'll start with an overview of data visualization concepts and simple web technologies, and then learn how to use D3, a JavaScript library that lets you express data as visual elements in a web page. Interactive Data Visualization for the Web makes these skills available at an introductory level for designers and visual artists without programming experience, journalists interested in the emerging data journalism processes, and others keenly interested in visualization and publicly available data sources. Get a practical introduction to data visualization, accessible for beginners Focus on web-based tools that help you publish your creations quickly to a wide audience Learn about interactivity so you can engage users in exploring your data

编码：隐匿在计算机软硬件背后的语言，ISBN：9787121181184，作者：（美）佩措尔德（Petzold,C.）著 左飞，薛佟佟译

《算法竞赛入门经典:训练指南》是《算法竞赛入门经典》的重要补充，旨在补充原书中没有涉及或者讲解得不够详细的内容，从而构建一个较完整的知识体系，并且用大量有针对性的题目，让抽象复杂的算法和数学具体化、实用化。《算法竞赛入门经典:训练指南》共6章，分别为算法设计基础、数学基础、实用数据结构、几何问题、图论算法与模型和更多算法专题，全书通过近200道例题深入浅出地介绍了上述领域的各个知识点、经典思维方式以及程序实现的常见方法和技巧，并在章末和附录中给出了丰富的分类习题，供读者查漏补缺和强化学习效果。

《别怕，excel vba其实很简单》考虑到大多数读者没有编程基础的实际情况，用浅显易懂的语言和生动形象的比喻，并配合大量插画，介绍excel中看似复杂的概念和代码、从简单的宏录制、vba编程环境和基础语法的介绍，到常用对象的操作与控制、excel事件的调用与控制、用户界面设计、代码调试与优化、都进行了形象的介绍。 《别怕，excel vba其实很简单》适合想提高工作效率的办公人员，特别是经常需要处理、分析大量数据的相关人员，以及财经专业的高校师生阅读。

In this innovative book, Professor Robert Harper offers a fresh perspective on the fundamentals of programming languages through the use of type theory. Whereas most textbooks on this subject emphasize taxonomy, Harper instead emphasizes genetics, examining the building blocks from which all programming languages are constructed. The result is an introduction to programming theory that is both accessible and practical.

自己编写一个操作系统，是许多程序员的梦想。也许有人曾经挑战过，但因为太难而放弃了。其实你错了，你的失败并不是因为编写操作系统太难，而是因为没有人告诉你那其实是一件很简单的事。那么，你想不想再挑战一次呢？ 这是一本兼具趣味性、实用性与学习性的书籍。作者从计算机的构造、汇编语言、C语言开始解说，让你在实践中掌握算法。在这本书的指导下，从零编写所有代码，30天后就可以制作出一个具有窗口系统的32位多任务操作系统。 本书以课题为主导，边做边玩，抛开晦涩难懂的语言，行文风格十分随性，还充满了各种欢乐的吐槽，适合操作系统爱好者和程序设计人员阅读。

《证析:大数据与基于证据的决策》的主题是Analytics，作者将一个英文新词Analytics译为“证析”借以指代在这个时代背景下对证据尤其是量化证据进行分析以影响决策的具体实践。《证析:大数据与基于证据的决策》更多关注数据对商业与社会领域决策的影响。《证析:大数据与基于证据的决策》在前言部分之外，分为上下两编。上编对证析所处的时代背景、证析对传统决策方式的挑战、证析在企业中的应用案例进行介绍，并着重介绍了实验在指导社会实践中的思想和实例。下编主要从证析对企业的价值、为了发挥证析的价值而在企业组织架构、考核体系、决策流程、组织文化等方面应有的考虑。除了介绍最新的管理思想与企业实践之外，因为证析的着眼点是数字与决策，所以贯穿全书也不可避免会涉及对科学研究方法的探讨。

In Hacker's Delight, Second Edition, Hank Warren once again compiles an irresistible collection of programming hacks: timesaving techniques, algorithms, and tricks that help programmers build more elegant and efficient software, while also gaining deeper insights into their craft. Warren's hacks are eminently practical, but they're also intrinsically interesting, and sometimes unexpected, much like the solution to a great puzzle. They are, in a word, a delight to any programmer who is excited by the opportunity to improve. Extensive additions in this edition include * A new chapter on cyclic redundancy checking (CRC), including routines for the commonly used CRC-32 code * A new chapter on error correcting codes (ECC), including routines for the Hamming code * More coverage of integer division by constants, including methods using only shifts and adds * Computing remainders without computing a quotient * More coverage of population count and counting leading zeros * Array population count * New algorithms for compress and expand * An LRU algorithm * Floating-point to/from integer conversions * Approximate floating-point reciprocal square root routine * A gallery of graphs of discrete functions * Now with exercises and answers

Now that storage and collection technologies are cheaper and more precise, methods for extracting relevant information from large datasets is within the reach any experienced programmer willing to crunch data. With this book, you'll learn machine learning and statistics tools in a practical fashion, using black-box solutions and case studies instead of a traditional math-heavy presentation. By exploring each problem in this book in depth - including both viable and hopeless approaches - you'll learn to recognize when your situation closely matches traditional problems. Then you'll discover how to apply classical statistics tools to your problem. Machine Learning for Hackers is ideal for programmers from private, public, and academic sectors.

Dive into Python's advanced possibilities, including algorithm analysis, graphs, scale-free networks, and cellular automata with this in-depth, hands-on guide. Whether you're an intermediate-level Python programmer, or a student of computational modeling, you'll examine data structures, complexity science, and other fascinating topics through a series of exercises, easy-to-understand explanations, and case studies. Think Complexity presents features that make Python such a simple and powerful language. Author Allen Downey provides code to help you get started, along with a solution for each exercise. With this book, you will: Work with graphs and graph algorithms, NumPy arrays and SciPy methods, basic signal processing and Fast Fourier Transform, and hash tables. Discover complexity science, the field that studies abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines. Explore the philosophy of science through the models and results in this book about the nature of scientific laws, theory choice, and realism and instrumentalism, and more.

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 which can be used on 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. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms and deep dives into several applications make the book equally useful for researchers, students and practitioners.

本书涵盖了五类重要的智能算法：搜索、推荐、聚类、分类和分类器组合，并结合具体的案例讨论了它们在Web应用中的角色及要注意的问题。除了第1章的概要性介绍以及第7章对所有技术的整合应用外，第2～6章以代码示例的形式分别对这五类算法进行了介绍。 本书面向的是广大普通读者，特别是对算法感兴趣的工程师与学生，所以对于读者的知识背景并没有过多的要求。本书中的例子和思想应用广泛，所以对于希望从业务角度更好地理解有关技术的技术经理、产品经理和管理层来说，本书也有一定的价值。

《算法设计与分析》为计算机科学技术专业核心课程“算法设计与分析”教材.全书以算法设计技术和分析方法为主线来组织各知识单元，主要内容包括基础知识、分治策略、动态规划、贪心法、回溯与分支限界、算法分析与问题的计算复杂度、NP完全性、近似算法、随机算法、处理难解问题的策略等。书中突出对问题本身的分析和求解方法的阐述，从问题建模、算法设计与分析、改进措施等方面给出适当的建议，同时也简要介绍了计算复杂性理论的核心内容和处理难解问题的一些新技术。 《算法设计与分析》有配套的学习指导与习题解析用书以及PPT电子教案。 《算法设计与分析》可作为大学计算机科学与技术、软件工程、信息安全、信息与计算机科学等专业本科生和研究生教学用书，也可以作为从事实际问题求解的算法设计与分析工作的参考书。

Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g., in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the stateof- the-art in certain areas. This monograph discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

The ability to interpret and act on the massive amounts of information locked in web and enterprise systems is critical to success in the modern business economy. R, a free software environment for statistical computing and graphics, is a comprehensive package that empowers developers and analysts to capture, process, and respond intelligently to statistical information. R in Action is the first book to present both the R system and the use cases that make it such a compelling package for business developers. The book begins by introducing the R language, and then moves on to various examples illustrating R's features. Coverage includes data mining methodologies, approaches to messy data, R's extensive graphical environment, useful add-on modules, and how to interface R with other software platforms and data management systems.

面对当前国内企事业单位及SOHO无线网络的飞速发展、智能手机等便携式设备的广泛使用、无线网络犯罪案例日益递增的发展现状，《无线网络安全攻防实战进阶》作为《无线网络安全攻防实战》一书的延续，依然以日趋严峻的无线网络安全为切入，从当前不为多数人所知的无线网络欺骗攻击案例讲起，由浅至深地剖析了无线网络安全及黑客技术涉及的各个深入方面，《无线网络安全攻防实战进阶》分为12章包括无线RADIUS认证体系搭建及攻防、蓝牙攻防实战、PDA/手机渗透及攻防实战、无线欺骗攻击深入实战、新技术高速破解、无线路由器攻防实战、无线网络犯罪取证、新型钓鱼等等。 全书内容衔接《无线网络安全攻防实战》一书，不再重复基础内容环节，而是以全新深入内容及全新案例来提升读者的实际水平。《无线网络安全攻防实战进阶》可以作为军警政机构无线安全人员、专业网络安全公司安全人员、无线评估及规划人员、企业及电子商务无线网络管理员、警务人员计算机犯罪鉴定及现场分析的有力参考，也可以作为高级黑客培训及网络安全认证机构的深入网络安全辅助教材，是安全技术爱好者、无线安全研究者、无线开发人员必备的参考宝典。