Neural Networks for Control brings together examples of all the most important paradigms for the application of neural networks to robotics and control. Primarily concerned with engineering problems and approaches to their solution through neurocomputing systems, the book is divided into three sections: general principles, motion control, and applications domains (with evaluations of the possible applications by experts in the applications areas.) Special emphasis is placed on designs based on optimization or reinforcement, which will become increasingly important as researchers address more complex engineering challenges or real biological-control problems.A Bradford Book. Neural Network Modeling and Connectionism series

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.

为什么印度的整体社会发展缓慢，而软件出口却独领风骚？为什么美国的IT公司不断裁人，同时又引进外国雇员？作者基于在印度、澳大利亚和马来西亚长达两年的人类学实地调查写作此书，指出其中关键在于IT产业中的“猎身”体系。它是指一个以印度为中心的全球化劳动力配置和管理体系。

FinTech developers and managers understand that the blockchain has the potential to disrupt the financial world. The blockchain allows the participants of a distributed system to agree on a common view of the system, to track changes in the system, in a reliable way. In the distributed systems community, agreement techniques have been known long before cryptocurrencies such as Bitcoin (where the term blockchain is borrowed) emerged. Various concepts and protocols exist, each with its own advantages and disadvantages. This book introduces the basic techniques when building fault-tolerant distributed systems, in a scientific way. We will present different protocols and algorithms that allow for fault-tolerant operation, and we will discuss practical systems that implement these techniques.

Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more.
With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.
You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples.

This text develops a comprehensive theory of programming languages based on type systems and structural operational semantics. Language concepts are precisely defined by their static and dynamic semantics, presenting the essential tools both intuitively and rigorously while relying on only elementary mathematics. These tools are used to analyze and prove properties of languages and provide the framework for combining and comparing language features. The broad range of concepts includes fundamental data types such as sums and products, polymorphic and abstract types, dynamic typing, dynamic dispatch, subtyping and refinement types, symbols and dynamic classification, parallelism and cost semantics, and concurrency and distribution. The methods are directly applicable to language implementation, to the development of logics for reasoning about programs, and to the formal verification language properties such as type safety. This thoroughly revised second edition includes exercises at the end of nearly every chapter and a new chapter on type refinements.

Text Classification, or the task of automatically assigning semantic categories to natural language text, has become one of the key methods for organizing online information. Since hand-coding classification rules is costly or even impractical, most modern approaches employ machine learning techniques to automatically learn text classifiers from examples. However, none of these conventional approaches combines good prediction performance, theoretical understanding, and efficient training algorithms. Based on ideas from Support Vector Machines (SVMs), Learning To Classify Text Using Support Vector Machines presents a new approach to generating text classifiers from examples. The approach combines high performance and efficiency with theoretical understanding and improved robustness. In particular, it is highly effective without greedy heuristic components. The SVM approach is computationally efficient in training and classification, and it comes with a learning theory that can guide real-world applications. Learning To Classify Text Using Support Vector Machines gives a complete and detailed description of the SVM approach to learning text classifiers, including training algorithms, transductive text classification, efficient performance estimation, and a statistical learning model of text classification. In addition, it includes an overview of the field of text classification, making it self-contained even for newcomers to the field. This book gives a concise introduction to SVMs for pattern recognition, and it includes a detailed description of how to formulate text-classification tasks for machine learning. Learning To Classify Text Using Support Vector Machines is designed as a reference for researchers and practitioners, and is suitable as a secondary text for graduate-level students in Computer Science within Machine Learning and Language Technology.

Graphical models in their modern form have been around since the late 1970s and appear today in many areas of the sciences. Along with the ongoing developments of graphical models, a number of different graphical modeling software programs have been written over the years. In recent years many of these software developments have taken place within the R community, either in the form of new packages or by providing an R interface to existing software. This book attempts to give the reader a gentle introduction to graphical modeling using R and the main features of some of these packages. In addition, the book provides examples of how more advanced aspects of graphical modeling can be represented and handled within R. Topics covered in the seven chapters include graphical models for contingency tables, Gaussian and mixed graphical models, Bayesian networks and modeling high dimensional data.

Traditional logic as a part of philosophy is one of the oldest scientific disciplines and can be traced back to the Stoics and to Aristotle. Mathematical logic, however, is a relatively young discipline and arose from the endeavors of Peano, Frege, and others to create a logistic foundation for mathematics. It steadily developed during the twentieth century into a broad discipline with several sub-areas and numerous applications in mathematics, informatics, linguistics and philosophy. This book treats the most important material in a concise and streamlined fashion. The third edition is a thorough and expanded revision of the former. Although the book is intended for use as a graduate text, the first three chapters can easily be read by undergraduates interested in mathematical logic. These initial chapters cover the material for an introductory course on mathematical logic, combined with applications of formalization techniques to set theory. Chapter 3 is partly of descriptive nature, providing a view towards algorithmic decision problems, automated theorem proving, non-standard models including non-standard analysis, and related topics. The remaining chapters contain basic material on logic programming for logicians and computer scientists, model theory, recursion theory, Godel's Incompleteness Theorems, and applications of mathematical logic. Philosophical and foundational problems of mathematics are discussed throughout the text. Each section of the seven chapters ends with exercises some of which of importance for the text itself. There are hints to most of the exercises in a separate file Solution Hints to the Exercises which is not part of the book but is available from the author's website.

中文书名：《计算机时代的爱情》
英文书名：Love in the Time of Algorithms
作 者：Dan Slater
代理公司：ANA
出版时间：2013年1月
代理地区：中国大陆、台湾
类 型：大众社科
版权已授：西班牙、英国、德国
本书从《大西洋月刊》（The Atlantic）摘录发行，引起巨大反响。
“网上约会”始于1965年的哈佛，从那以后网上约会产业为着全美三分之一，大约8千万的单身人士服务。这个现象原来被当做寂寞和绝望的归宿，但如今却被人们所接受。
丹·斯莱特（Dan Slater）为我们讲述了网上约会是如何推动一轮新的性革命。这要归功于网络的高效性，那些情投意合的伴侣不再仅属于小型的群体。高效性和控制性改变了人们对私人生活可能性的看法，也让我们重新感受稳定性和承诺。
斯莱特在他的书中将历史、心理学以及对网站建设者及使用者的采访结合起来，把读者带进了这个令人称奇产业的幕后世界。相亲网站利用的正是人们对爱的渴求和向往。但是您想不想知道这些网站的建设者对搭建这个虚拟世界的利润、有限性和欲望的本质是如何看待的呢？以及他们如何理解人们的线下情缘？
媒体评价：
“本书从哈佛有趣但别扭的网上约会开始，逐渐走向专业，到今天在迈阿密的时髦派对，斯莱特专业并娴熟地带领我们经历了我们这个时代最大的求爱变革（与之前六七十年代简·奥斯汀似的恋爱方式相比）。作者以独到的探究手法显示了科技也许重新塑造了人类存在最重要的一面：爱情。”
----万妮莎·格里奥迪亚斯（VANESSA GRIGORIADIS），《纽约杂志》（New York Magazine）编辑
“五种关系中的一种如今始于电脑屏幕前。但是我们是如何到达这里的？丹·斯莱特（Dan Slater）带我们窥探了这些相亲主管们的独特思维，这些人建立了这个产业，与此同时这些人的生活因为相亲革命而发生永久性的改变。作为这些管理者的其中一位，我想我知道这个故事。但是斯莱特带着幽默和新鲜的视角将改变你对数字世界里爱情的看法。”
----山姆·亚甘（SAM YAGAN），OkCupid创建者及执行总裁
“这是一本详细介绍了计算机科学和创业者努力解决人们生活中最长期存在的挑战之一：找到合适的人生伴侣的优秀读物。书里既没有诋毁也没有赞扬网上相亲这种作为，斯莱特的清楚地表明寻找伴侣的方式如今已经永久性地改变了，无论你喜不喜欢，我们可能无法回头。”
----蒂姆·吴（TIM WU），《信息帝国的兴衰》（The Master Switch）作者
“《计算机时代的爱情》（Love in the Time of Algorithms）贯穿了整个网上相亲的世界，非常精彩。本书充满了人们如何习惯（或者不习惯）网络爱情的轶事，但是与此同时斯莱特同样提出了一个大问题：科学能够预测爱情吗？以及相亲网站上那一摞摞的相亲对象对于未来的人类关系又意味着什么？”
----本瑟尼·麦克林（BETHANY McLEAN），《最聪明的人》（The Smartest Guys in the Room）作者
“同如今大量充斥在数字世界的其他商品一样，爱情也开始蜂拥而来。但是，正如斯莱特娴熟又风趣地向我们指出，把爱情拿到算术法则当中去改变的将不仅是我们最终跟谁走在了一起，更是我们如何看待伴侣、关系、人类意志甚至命运。”
----道格拉斯·洛西科夫（DOUGLAS RUSHKOFF），《生命公司》（Life Inc.）及《当今震惊》（Present Shock）作者
“网络会为我们带来网上相亲以及使科技在我们的爱情生活中蔓延吗？！这本书对我们所在的这个科技-爱情的世界进行艺术性的检验，丹·斯莱特（Dan Slater）为我们打开视野，介绍了永久改变我们行为以及渴望的方式。”
----杰西卡·马萨（JESSICA MASSA）及瑞贝卡·魏根特（REBECCA WIEGAND），“The Gaggle & WTF Is Up With My Love Life?!”共同创立者
中文简体版权代理：
安德鲁•纳伯格联合国际有限公司北京代表处
电子邮箱：JHuang@nurnberg.com.cn
网址：http://www.nurnberg.com.cn
新浪微博：http://weibo.com/nurnberg
豆瓣小站：http://site.douban.com/110577/

Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues. The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics.

Genetic algorithms are founded upon the principle of evolution, i.e., survival of the fittest. Hence evolution programming techniques, based on genetic algorithms, are applicable to many hard optimization problems, such as optimization of functions with linear and nonlinear constraints, the traveling salesman problem, and problems of scheduling, partitioning, and control. The importance of these techniques is still growing, since evolution programs are parallel in nature, and parallelism is one of the most promising directions in computer science.
The book is self-contained and the only prerequisite is basic undergraduate mathematics. This third edition has been substantially revised and extended by three new chapters and by additional appendices containing working material to cover recent developments and a change in the perception of evolutionary computation.

'A Byte of Python' is a book on programming using the Python language. It serves as a tutorial or guide to the Python language for a beginner audience. If all you know about computers is how to save text files, then this is the book for you.

This text covers topics in algebraic geometry and commutative algebra with a strong perspective toward practical and computational aspects. The first four chapters form the core of the book. A comprehensive chart in the Preface illustrates a variety of ways to proceed with the material once these chapters are covered. In addition to the fundamentals of algebraic geometry—the elimination theorem, the extension theorem, the closure theorem and the Nullstellensatz—this new edition incorporates several substantial changes, all of which are listed in the Preface. The largest revision incorporates a new Chapter (ten), which presents some of the essentials of progress made over the last decades in computing Gröbner bases. The book also includes current computer algebra material in Appendix C and updated independent projects (Appendix D).
The book may serve as a first or second course in undergraduate abstract algebra and with some supplementation perhaps, for beginning graduate level courses in algebraic geometry or computational algebra. Prerequisites for the reader include linear algebra and a proof-oriented course.It is assumed that the reader has access to a computer algebra system. Appendix C describes features of Maple™, Mathematica® and Sage, as well as other systems that are most relevant to the text. Pseudocode is used in the text; Appendix B carefully describes the pseudocode used.

Aimed at undergraduate mathematics and computer science students, this book is an excellent introduction to a lot of problems of discrete mathematics. It discusses a number of selected results and methods, mostly from areas of combinatorics and graph theory, and it uses proofs and problem solving to help students understand the solutions to problems. Numerous examples, figures, and exercises are spread throughout the book.

These notes were first used in an introductory course team taught by the authors at Appalachian State University to advanced undergraduates and beginning graduates. The text was written with four pedagogical goals in mind: offer a variety of topics in one course, get to the main themes and tools as efficiently as possible, show the relationships between the different topics, and include recent results to convince students that mathematics is a living discipline.