《高等学校教材：SPSS统计分析高级教程（第2版）》以IBMSPSSStatistics20中文版为基础，全面、系统地介绍了各种多变量统计模型、多元统计分析模型、智能统计分析方法的原理和软件实现。在书中作者结合自身多年的统计分析实战和SPss行业应用经验，侧重于对统计新方法、新观点的讲解。在保证统计理论严谨的同时，又充分注重了文字的浅显易懂，使《高等学校教材：SPSS统计分析高级教程（第2版）》更加易学易用。
《高等学校教材：SPSS统计分析高级教程（第2版）》是一本如何使用SPss进行高级统计分析的指导书。读者可在www.StatStar.com下载书中案例数据，从而完整地重现全部分析内容，并可进一步在新浪微博与作者、其他读者进行讨论。
《高等学校教材：SPSS统计分析高级教程（第2版）》适合于已具备统计分析基础知识的读者阅读，可作为高等学校各专业高年级本科生、研究生的统计学教材或参考书，以及市场营销、金融、财务、人力资源管理等行业中需要做数据分析的人士，或从事咨询、研究、分析等专业人士的参考书。

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.

Every programmer and software project manager must master the art of creating reusable software modules; they are the building blocks of large, reliable applications. Unlike some modern object-oriented languages, C provides little linguistic support or motivation for creating reusable application programming interfaces (APIs). While most C programmers use APIs and the libraries that implement them in almost every application they write, relatively few programmers create and disseminate new, widely applicable APIs. C Interfaces and Implementations shows how to create reusable APIs using interface-based design, a language-independent methodology that separates interfaces from their implementations. This methodology is explained by example. The author describes in detail 24 interfaces and their implementations, providing the reader with a thorough understanding of this design approach.

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.

神经网络是一种模拟人脑的神经网络，以期能够实现类人工智能的机器学习
技术。
本书揭示神经网络背后的概念，并介绍如何通过Python实现神经网络。全书
分为3章和两个附录。第1章介绍了神经网络中所用到的数学思想。第2章介绍使
用Python实现神经网络，识别手写数字，并测试神经网络的性能。第3章带领读
者进一步了解简单的神经网络，观察已受训练的神经网络内部，尝试进一步改善
神经网络的性能，并加深对相关知识的理解。附录分别介绍了所需的微积分知识
和树莓派知识。
本书适合想要从事神经网络研究和探索的读者学习参考，也适合对人工智
能、机器学习和深度学习等相关领域感兴趣的读者阅读。

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。

In their second collaboration, biographers Jimmy Soni and Rob Goodman present the story of Claude Shannon—one of the foremost intellects of the twentieth century and the architect of the Information Age, whose insights stand behind every computer built, email sent, video streamed, and webpage loaded. Claude Shannon was a groundbreaking polymath, a brilliant tinkerer, and a digital pioneer. He constructed the first wearable computer, outfoxed Vegas casinos, and built juggling robots. He also wrote the seminal text of the digital revolution, which has been called “the Magna Carta of the Information Age.” In this elegantly written, exhaustively researched biography, Soni and Goodman reveal Claude Shannon’s full story for the first time. With unique access to Shannon’s family and friends, A Mind at Play brings this singular innovator and always playful genius to life.

本书由系统架构领域3位领军人物亲笔撰写，该领域资深专家Norman R. Augustine作序推荐，Amazon全五星评价。
全书共分四部分。
第一部分（第1~3章）的重点是引出系统架构。第1章通过一些范例来展示架构理念，指出良好的架构，并给出本书的概要；第2章列出进行系统分析必备的思路；第3章给出分析系统架构所用的思维模式。
第二部分（第4~8章）着重对架构进行分析。第4章讨论系统的形式；第5章讨论系统的功能；第6章讲解形式与功能之间的映射，并以此给出系统架构的定义；第7章研究如何从独立于解决方案的功能陈述中衍生出系统；第8章演示怎样把这些概念汇聚成一套架构。
第三部分（第9~13章）讲解如何为复杂的系统定义架构。第9章从任务和可交付成果这两方面来概述架构师的职责；第10章探讨如何把组织机构方面的接口当成在架构中减少歧义的契机；第11章讲述如何用系统化的方式来捕获利益相关者的需求，并把它们转换成系统目标；第12章提出一些能够帮助架构师更有创意地构思并选择概念的手段；第13章讲述在开发系统时管理复杂度的一些办法。
第四部分（第14~16章）探寻帮助架构师做决策的各种计算方法及工具所具备的潜力。第14章把系统架构的过程当成一种决策制定的过程来进行讲解；第15章讲解如何对架构权衡空间中的信息进行综合；第16章演示怎样把架构决策编码成一套模型，使计算机可以根据该模型自动生成权衡空间并对其进行探索。

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Clarifies both traditional methods and current, popular algorithms (e.g. neural nets, random forests)
Written by two world-leading researchers
Addressed to all fields that work with data

A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence
“Correlation is not causation.” This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality–the study of cause and effect–on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl’s work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.

《数字创世纪:人工生命的新科学》对计算机和生物学交叉的前沿科学——人工生命进行了较详细地描述，从人工生命的起源、研究方法、形式、主张等不同方面进行描述，是国内外第一本系统地、全面地阐述人工生命的生命观的著作。系统论述了人工生命思想的产生和发展过程，其中20世纪中后期到现在的发展是《数字创世纪:人工生命的新科学》首次论述的。《数字创世纪:人工生命的新科学》写作规范，各部分之间逻辑联系紧密。虽然《》涉及的科学内容艰深，但由于作者观点明确，深入浅出，论证清晰，因此，《数字创世纪:人工生命的新科学》可读性强，有一定基础的研究者就可以理解。

从本书第一版出版以来，一直好评如潮，被国外许多大学选作自然语言处理或计算语言学的教材，被认为该领域教材的“黄金标准”。
本书第一版综合了自然语言处理、计算语言学和语音识别的内容，全面论述计算机自然语言处理，深入探讨计算机处理自然语言的词汇、句法、语义、语用等各个方面的问题，介绍了自然语言处理的各种现代技术。该版对于第一版做了全面的改写，增加了大量反映自然语言处理最新成就的内容，特别是增加了语音处理和统计技术方面的内容，全书面貌为之一新。本书四大特色： 覆盖全面 强调实用 注重评测 语料为本内容简介本书全面论述了自然语言处理技术。
本书在第一版的基础上增加了自然语言处理的最新成就，特别是增加了语音处理和统计技术方面的内容，全书面貌为之一新。本书共分五个部分。第一部分“词汇的计算机处理”，讲述单词的计算机处理，包括单词切分、单词的形态学、最小编辑距离、词类，以及单词计算机处理的各种算法，包括正则表达式、有限状态自动机、有限状态转录机、N元语法模型、隐马尔可夫模型、最大熵模型等。第二部分“语音的计算机处理”，介绍语音学、语音合成、语音自动识别以及计算音系学。第三部分“句法的计算机处理”，介绍英语的形式语法，讲述句法剖析的主要算法，包括CKY剖析算法、Earley剖析算法、统计剖析，并介绍合一与类型特征结构、Chomsky层级分类、抽吸引理等分析工具。第四部分“语义和语用的计算机处理”，介绍语义的各种表示方法、计算语义学、词汇语义学、计算词汇语义学，并介绍同指、连贯等计算机话语分析问题。第五部分“应用”，讲述信息抽取、问答系统、自动文摘、对话和会话智能代理、机器翻译等自然语言处理的应用技术。本书写作风格深入浅出，实例丰富，引人入胜。本书可作为高等学校自然语言处理或计算语言学的本科生和研究生的教材，也可以作为从事人工智能、自然语言处理等领域的研究人员和技术人员的必备参考。

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.

本书是深度学习真正意义上的入门书，深入浅出地剖析了深度学习的原理和相关技术。书中使用Python3，尽量不依赖外部库或工具，从基本的数学知识出发，带领读者从零创建一个经典的深度学习网络，使读者在此过程中逐步理解深度学习。书中不仅介绍了深度学习和神经网络的概念、特征等基础知识，对误差反向传播法、卷积神经网络等也有深入讲解，此外还介绍了深度学习相关的实用技巧，自动驾驶、图像生成、强化学习等方面的应用，以及为什么加深层可以提高识别精度等“为什么”的问题。

该书是一本von Neumann关于“自复制自动机”的研究论文集，由von Neumann在密西根大学的助手Arthur Burk（大名鼎鼎的John Holland的博士导师）整理编辑。
这本书的意义不仅仅在于它实际上开创了“人工生命”、“细胞自动机”等多门复杂性研究的分支。更重要的是，该书将“自我复制”作为生命的一个本质特征加以数学建模的研究。而这套理论和可计算性理论、歌德尔定理以及热力学深深地联系到了一起。这一点也许对于我们研究复杂系统的人来说仍具有重要的意义。