《韩非子》主要版本书影(插页)
导读(周勋初)
凡例
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韩非传
乾道本《韩非子》目录
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疯狂的年代，理性的思考——《韩非子校注》编写始末(周勋初)
《韩非子》人名索引
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《梦溪笔谈》是中国笔记研究中被研究最多最深的本子之一，一直成为中国科学史研究的重点。然本书内容涉及甚广，人事、官政、权智，下至闾巷之言，亦是研究北宋时期典制礼仪、财役制度、军事制度的重要史料。此次整理，《笔谈》部分以文物出版社影印的元刊本为底本，《补笔谈》、《续笔谈》部分以清光绪三十年陶氏爱庐本为底本。为便于读者阅读，本书目录的卷首条目标题按卷排比，文中出现的人物、典籍、年号编为索引，附于卷末。有价值的各种版本序跋和《沈括事略》收于附录，以见全貌。

大学·中庸，ISBN：9787101052763，作者：王国轩

The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.
This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.

【《文明之光》系列荣获由中宣部、中国图书评论学会和中央电视台联合推选的2014“中国好书”奖】
吴军博士从对人类文明产生了重大影响却在过去被忽略的历史故事里，选择了有意思的几十个片段特写，以人文和科技、经济结合的视角，有机地展现了一幅人类文明发展的宏大画卷。
《文明之光 》系列大致按照从地球诞生到近现代的顺序讲述了人类文明进程的各个阶段，每个章节相对独立，全景式地展现了人类文明发展历程中的多样性。
《文明之光》系列第二册讲述了从近代科学兴起，到工业革命时代，以及原子能应用这一历史阶段，共八个专题。
看完这个系列，读者朋友可以体会到，在漫长的历史长河中，人类社会虽然不断遇到一些困难和问题，但人类总是会有办法解决它们，我们要成为一个“理性乐观派”；其次是能够认识到普通人对文明发展的贡献，每个人都可以成为贡献者。
【编辑推荐】
一曲理性乐观者的牧歌 通俗易懂的世界通史入门读物
娓娓道来几十段精彩的文明史片段，闪耀人类文明的三卷赞歌！
作者以人文与科技、经济结合的视角，选取对人类历史发展产生重大影响的人或事件：大航海，文艺复兴，古代罗马，美第奇家族，瓷器，牛顿，爱迪生，航天飞机，原子能，绘画、音乐，计算机时代……透过有趣易懂的故事式的讲解，揭示人类文明不断解决问题、曲折前行的辉煌历程，以及平凡人等对社会进步的巨大贡献。在阅读过程中，读者既能够增长历史知识，也可以体会到人类文明发展历程的多样性。
在具体的讲述上，作者以科学的态度提及不同的观点，并不只执一家之言或是轻易给出一个结论，比如评价宋朝就提到了钱穆、陈寅恪、费正清的不同观点。正是这种贯穿始终的科学态度，让这本书给人启发的同时也令人信服。
万科集团董事长王石先生、美国著名华裔物理学家张首晟教授分别为《文明之光》作序，对本书给予了高度评价。

Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. The book covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers. Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.
The first edition became a widely used text in universities worldwide as well as the standard reference for professionals. The second edition featured new chapters on the role of algorithms, probabilistic analysis and randomized algorithms, and linear programming. The third edition has been revised and updated throughout. It includes two completely new chapters, on van Emde Boas trees and multithreaded algorithms, and substantial additions to the chapter on recurrences (now called "Divide-and-Conquer"). It features improved treatment of dynamic programming and greedy algorithms and a new notion of edge-based flow in the material on flow networks. Many new exercises and problems have been added for this edition.
As of the third edition, this textbook is published exclusively by the MIT Press.

Ross's classic bestseller, Introduction to Probability Models, has been used extensively by professionals and as the primary text for a first undergraduate course in applied probability. It provides an introduction to elementary probability theory and stochastic processes, and shows how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. With the addition of several new sections relating to actuaries, this text is highly recommended by the Society of Actuaries. Ancillary list: Instructor's Manual - http://textbooks.elsevier.com/web/manuals.aspx?isbn=9780123743886 Student Solutions Manual - http://www.elsevierdirect.com/product.jsp?isbn=9780123756862#42 Sample Chapter, eBook - http://www.elsevierdirect.com/product.jsp?isbn=9780123756862
New to this Edition: 65% new chapter material including coverage of finite capacity queues, insurance risk models and Markov chains Contains compulsory material for new Exam 3 of the Society of Actuaries containing several sections in the new exams Updated data, and a list of commonly used notations and equations, a robust ancillary package, including a ISM, SSM, test bank, and companion website Includes SPSS PASW Modeler and SAS JMP software packages which are widely used in the field Hallmark features: Superior writing style Excellent exercises and examples covering the wide breadth of coverage of probability topics Real-world applications in engineering, science, business and economics

本书精选了万维钢老师的文章和书评，以“用理工科思维理解世界”为导向。作者常用有趣的实验、数据来解读感性的事物，其理工科思维涉及行为经济学、认知心理学、社会学、统计学、物理等许多学科，以前沿的科学视角解读生活，为人们提供了认知的新方法。读完本书相当于精读了十几本经过筛选 、再创作及通俗化处理的巨著，不仅有趣还十分有营养。

The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges.
* Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.

A timeless introduction to the field and a landmark in symbolic logic, showing that classical logic can be treated algebraically.

The Rise and Fall of the Great Powers: Economic Change and Military Conflict From 1500 to 2000, by Paul Kennedy, first published in 1987, explores the politics and economics of the Great Powers from 1500 to 1980 and the reason for their decline. It then continues by forecasting the positions of China, Japan, the European Economic Community (EEC), the Soviet Union and the United States through the end of the 20th century

For over thirty years David S. Landes's The Unbound Prometheus has offered an unrivalled history of industrial revolution and economic development in Europe. Now, in this updated edition, the author reframes and reasserts his original arguments in the light of debates about globalisation and comparative economic growth. The book begins with a classic account of the characteristics, progress, and political, economic and social implications of the Industrial Revolution in Britain, France and Germany. Professor Landes here raises the much-debated question: why was Europe the first to industrialise? He then charts the economic history of the twentieth-century: the effect of the First World War in accelerating the dissolution of the old international economy; the economic crisis of 1929-32; Europe's recovery and unprecedented economic growth following the Second World War. He concludes that only by continuous industrial revolution can Europe and the world sustain itself in the years ahead.

In this magisterial new study, Peter Burke explores the social and cultural history of the languages spoken or written in Europe between the invention of printing and the French Revolution, arguing that, from a linguistic point of view, 1450 to 1789 should be regarded as a distinct period. One major theme of the book is the relation between languages and communities (regions, churches, occupations and genders as well as nations) and the place of language as a way of identifying others as well as a symbol of one's own identity. A second, linked theme is that of competition: between Latin and the vernaculars, between different vernaculars, dominant and subordinate, and finally between different varieties of the same vernacular, such as standard languages and dialects. Written by one of Europe's leading cultural historians, this book restores the history of the many languages of Europe in a large variety of contexts.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.