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.

In The Algebraic Mind , Gary Marcus attempts to integrate two theories about how the mind works, one that says that the mind is a computer-like manipulator of symbols, and another that says that the mind is a large network of neurons working together in parallel. Resisting the conventional wisdom that says that if the mind is a large neural network it cannot simultaneously be a manipulator of symbols, Marcus outlines a variety of ways in which neural systems could be organized so as to manipulate symbols, and he shows why such systems are more likely to provide an adequate substrate for language and cognition than neural systems that are inconsistent with the manipulation of symbols. Concluding with a discussion of how a neurally realized system of symbol-manipulation could have evolved and how such a system could unfold developmentally within the womb, Marcus helps to set the future agenda of cognitive neuroscience.

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.Olivier Chapelle and Alexander Zien are Research Scientists and Bernhard Scholkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tubingen. Scholkopf is coauthor of Learning with Kernels (MIT Press, 2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by The MIT Press.

#1 New York Times Bestseller
“Significant...The book is both instructive and surprisingly moving.” —The New York Times
Ray Dalio, one of the world’s most successful investors and entrepreneurs, shares the unconventional principles that he’s developed, refined, and used over the past forty years to create unique results in both life and business—and which any person or organization can adopt to help achieve their goals.
In 1975, Ray Dalio founded an investment firm, Bridgewater Associates, out of his two-bedroom apartment in New York City. Forty years later, Bridgewater has made more money for its clients than any other hedge fund in history and grown into the fifth most important private company in the United States, according to Fortune magazine. Dalio himself has been named to Time magazine’s list of the 100 most influential people in the world. Along the way, Dalio discovered a set of unique principles that have led to Bridgewater’s exceptionally effective culture, which he describes as “an idea meritocracy that strives to achieve meaningful work and meaningful relationships through radical transparency.” It is these principles, and not anything special about Dalio—who grew up an ordinary kid in a middle-class Long Island neighborhood—that he believes are the reason behind his success.
In Principles, Dalio shares what he’s learned over the course of his remarkable career. He argues that life, management, economics, and investing can all be systemized into rules and understood like machines. The book’s hundreds of practical lessons, which are built around his cornerstones of “radical truth” and “radical transparency,” include Dalio laying out the most effective ways for individuals and organizations to make decisions, approach challenges, and build strong teams. He also describes the innovative tools the firm uses to bring an idea meritocracy to life, such as creating “baseball cards” for all employees that distill their strengths and weaknesses, and employing computerized decision-making systems to make believability-weighted decisions. While the book brims with novel ideas for organizations and institutions, Principles also offers a clear, straightforward approach to decision-making that Dalio believes anyone can apply, no matter what they’re seeking to achieve.
Here, from a man who has been called both “the Steve Jobs of investing” and “the philosopher king of the financial universe” (CIO magazine), is a rare opportunity to gain proven advice unlike anything you’ll find in the conventional business press.

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.

* Capturing a wealth of experience about the design of object-oriented software, four top-notch designers present a catalog of simple and succinct solutions to commonly occurring design problems. Previously undocumented, these 23 patterns allow designers to create more flexible, elegant, and ultimately reusable designs without having to rediscover the design solutions themselves. * The authors begin by describing what patterns are and how they can help you design object-oriented software. They then go on to systematically name, explain, evaluate, and catalog recurring designs in object-oriented systems. With Design Patterns as your guide, you will learn how these important patterns fit into the software development process, and how you can leverage them to solve your own design problems most efficiently.

You're not alone. At any given moment, somewhere in the world someone struggles with the same software design problems you have. You know you don't want to reinvent the wheel (or worse, a flat tire), so you look to Design Patterns--the lessons learned by those who've faced the same problems. With Design Patterns, you get to take advantage of the best practices and experience of others, so that you can spend your time on... something else. Something more challenging. Something more complex. Something more fun. You want to learn about the patterns that matter--why to use them, when to use them, how to use them (and when NOT to use them). But you don't just want to see how patterns look in a book, you want to know how they look "in the wild". In their native environment. In other words, in real world applications. You also want to learn how patterns are used in the Java API, and how to exploit Java's built-in pattern support in your own code. You want to learn the real OO design principles and why everything your boss told you about inheritance might be wrong (and what to do instead). You want to learn how those principles will help the next time you're up a creek without a design paddle pattern. Most importantly, you want to learn the "secret language" of Design Patterns so that you can hold your own with your co-worker (and impress cocktail party guests) when he casually mentions his stunningly clever use of Command, Facade, Proxy, and Factory in between sips of a martini. You'll easily counter with your deep understanding of why Singleton isn't as simple as it sounds, how the Factory is so often misunderstood, or on the real relationship between Decorator, Facade and Adapter. With Head First Design Patterns, you'll avoid the embarrassment of thinking Decorator is something from the "Trading Spaces" show. Best of all, in a way that won't put you to sleep! We think your time is too important (and too short) to spend it struggling with academic texts. If you've read a Head First book, you know what to expect - a visually-rich format designed for the way your brain works. Using the latest research in neurobiology, cognitive science, and learning theory, Head First Design Patterns will load patterns into your brain in a way that sticks. In a way that lets you put them to work immediately. In a way that makes you better at solving software design problems, and better at speaking the language of patterns with others on your team.

Everyone talks about style, but no one explains it. The authors of this book do; and in doing so, they provoke the reader to consider style, not as an elegant accessory of effective prose, but as its very heart. At a time when writing skills have virtually disappeared, what can be done? If only people learned the principles of verbal correctness, the essential rules, wouldn't good prose simply fall into place? Thomas and Turner say no. Attending to rules of grammar, sense, and sentence structure will no more lead to effective prose than knowing the mechanics of a golf swing will lead to a hole-in-one. Furthermore, ten-step programs to better writing exacerbate the problem by failing to recognize, as Thomas and Turner point out, that there are many styles with different standards. In the first half of "Clear and Simple", the authors introduce a range of styles - reflexive, practical, plain, contemplative, romantic, prophetic, and others - contrasting them to classic style. Its principles are simple: The writer adopts the pose that the motive is truth, the purpose is presentation, the reader is an intellectual equal, and the occasion is informal. Classic style is at home in everything from business memos to personal letters, from magazine articles to university writing. The second half of the book is a tour of examples - the exquisite and the execrable - showing what has worked and what hasn't. Classic prose is found everywhere: from Thomas Jefferson to Junichiro Tanizaki, from Mark Twain to the observations of an undergraduate. Here are many fine performances in classic style, each clear and simple as the truth.

After two decades of hostile confrontation, China and the United States initiated negotiations in the early 1970s to normalize relations. Senior officials of the Nixon, Ford, Carter, and Reagan administrations had little experience dealing with the Chinese, but they soon learned that their counterparts from the People’s Republic were skilled negotiators.
This study of Chinese negotiating behavior explores the ways senior officials of the PRC―Mao Zedong, Zhou Enlai, Deng Xiaoping, and others―managed these high-level political negotiations with their new American “old friends.” It follows the negotiating process step by step, and concludes with guidelines for dealing with Chinese officials.
Originally written for the RAND Corporation, this study was classified because it drew on the official negotiating record. It was subsequently declassified, and RAND published the study in 1995. For this edition, Solomon has added a new introduction, and Chas Freeman has written an interpretive essay describing the ways in which Chinese negotiating behavior has, and has not, changed since the original study. The bibiliography has been updated as well.

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The incredible national bestseller that is changing people's lives -- and increasing their net worth! CAN YOU SPOT THE MILLIONAIRE NEXT DOOR? Who are the rich in this country? What do they do? Where do they shop? What do they drive? How do they invest? Where did their ancestors come from? How did they get rich? Can I ever become one of them? Get the answers in The Millionaire Next Door, the never-before-told story about wealth in America. You'll be surprised at what you find out....

Tony Gaddis's accessible, step-by-step presentation helps beginning students understand the important details necessary to become skilled programmers at an introductory level. Gaddis motivates the study of both programming skills and the C++ programming language by presenting all the details needed to understand the "how" and the "why"--but never losing sight of the fact that most beginners struggle with this material. His approach is both gradual and highly accessible, ensuring that students understand the logic behind developing high-quality programs. In Starting Out with C++: From Control Structures through Objects, Gaddis covers control structures, functions, arrays, and pointers before objects and classes. As with all Gaddis texts, clear and easy-to-read code listings, concise and practical real-world examples, and an abundance of exercises appear in every chapter. This text is intended for either a one-semester accelerated introductory course or a traditional two-semester sequence covering C++ programming.

本书勾勒了1720-1830年间，西学在日本的发展与实践。这段时期的前后两个年份中，前者标志着官方开始关注西学，后者则是驱逐西博尔德出境的年份。如果说日本的近代化始于明治维新的话，那么，从1868年回溯到1720年的近150年，则可以说是酝酿这场近代化的漫长的准备期。彼时的知识分子奋起反抗锁国，拼命探索来自国外的新知识。像本多利明这样有着充沛精力并极富热情的人们，促使日本发生了令人惊叹的变化。
本多利明是本书的核心人物，其手稿中的任何一页都充分表明他已进入一个新的时代，即近代日本。在他的书中，人们发现了一种新的精神：不安、好奇和接受。本多好奇于新的发现，乐于开阔眼界，甚至带着某种喜悦揭示日本只不过是大千世界中的一座小岛而已。对那些认为中华文明才是源远流长的日本人，他公然宣称拥有数千年历史的埃及才更为久远、更加崇高。他发现，世界充满了奇妙的事物，他坚持认为日本应该取诸所长。他尝试用西方人的视角观察日本，明了时局必将扭转，如今的国家已被严重耗费德行和物力。即便如此，他仍确信日本一定能够成为世界大国之一。

This book is meant to provide an introduction to vectors, matrices, and least squares methods, basic topics in applied linear algebra. Our goal is to give the beginning student, with little or no prior exposure to linear algebra, a good grounding in the basic ideas, as well as an appreciation for how they are used in many applications, including data fitting, machine learning and artificial intelligence, to-mography, navigation, image processing, finance, and automatic control systems.
The background required of the reader is familiarity with basic mathematical notation. We use calculus in just a few places, but it does not play a critical role and is not a strict prerequisite. Even though the book covers many topics that are traditionally taught as part of probability and statistics, such as fitting mathematical models to data, no knowledge of or background in probability and statistics is needed.
The book covers less mathematics than a typical text on applied linear algebra. We use only one theoretical concept from linear algebra, linear independence, and only one computational tool, the QR factorization; our approach to most applications relies on only one method, least squares (or some extension). In this sense we aim for intellectual economy: With just a few basic mathematical ideas, con-cepts, and methods, we cover many applications. The mathematics we do present, however, is complete, in that we carefully justify every mathematical statement. In contrast to most introductory linear algebra texts, however, we describe many applications, including some that are typically considered advanced topics, like document classification, control, state estimation, and portfolio optimization.
The book does not require any knowledge of computer programming, and can be used as a conventional textbook, by reading the chapters and working the exercises that do not involve numerical computation. This approach however misses out on one of the most compelling reasons to learn the material: You can use the ideas and methods described in this book to do practical things like build a prediction model from data, enhance images, or optimize an investment portfolio. The growing power of computers, together with the development of high level computer languages and packages that support vector and matrix computation, have made it easy to use the methods described in this book for real applications. For this reason we hope that every student of this book will complement their study with computer programming exercises and projects, including some that involve real data. This book includes some generic exercises that require computation; additional ones, and the associated data files and language-specific resources, are available online.
If you read the whole book, work some of the exercises, and carry out computer exercises to implement or use the ideas and methods, you will learn a lot. While there will still be much for you to learn, you will have seen many of the basic ideas behind modern data science and other application areas. We hope you will be empowered to use the methods for your own applications.
The book is divided into three parts. Part I introduces the reader to vectors, and various vector operations and functions like addition, inner product, distance, and angle. We also describe how vectors are used in applications to represent word counts in a document, time series, attributes of a patient, sales of a product, an audio track, an image, or a portfolio of investments. Part II does the same for matrices, culminating with matrix inverses and methods for solving linear equa-tions. Part III, on least squares, is the payoff, at least in terms of the applications. We show how the simple and natural idea of approximately solving a set of over-determined equations, and a few extensions of this basic idea, can be used to solve many practical problems.
The whole book can be covered in a 15 week (semester) course; a 10 week (quarter) course can cover most of the material, by skipping a few applications and perhaps the last two chapters on nonlinear least squares. The book can also be used for self-study, complemented with material available online. By design, the pace of the book accelerates a bit, with many details and simple examples in parts I and II, and more advanced examples and applications in part III. A course for students with little or no background in linear algebra can focus on parts I and II, and cover just a few of the more advanced applications in part III. A more advanced course on applied linear algebra can quickly cover parts I and II as review, and then focus on the applications in part III, as well as additional topics.
We are grateful to many of our colleagues, teaching assistants, and students for helpful suggestions and discussions during the development of this book and the associated courses. We especially thank our colleagues Trevor Hastie, Rob Tibshirani, and Sanjay Lall, as well as Nick Boyd, for discussions about data fitting and classification, and Jenny Hong, Ahmed Bou-Rabee, Keegan Go, David Zeng, and Jaehyun Park, Stanford undergraduates who helped create and teach the course EE103. We thank David Tse, Alex Lemon, Neal Parikh, and Julie Lancashire for carefully reading drafts of this book and making many good suggestions.

It took me a pretty long time to really get Git. As I’ve continued to use Git more and more where I work, I’ve found myself trying to teach people what it is and why we use it over and over again, and the reality is that Git generally has a pretty steep learning curve compared to many other systems. I’ve seen case after case of devel- opers who love Git after they finally understand it, but getting to that point is often somewhat painstaking.
This book is aimed at the developer who does not particularly like Subversion, Perforce or whatever SCM system they are currently using, has heard good things about Git, but doesn’t know where to start or why it’s so wonderful. It is meant to explain Git as simply as possible in a clean, concise, easily readable volume. My goal is to help you understand Git internals as well as usage at a fundamental level by the time you finish this book.
To accomplish this, I’m starting the book out (after the introduction) with a section about what Git actually does, rather than how to use it. I found that I didn’t really understand Git and had many problems using it until I understood what it was actually doing at a low level, rather than thinking of it as a different, weird SVN-like system.

Known as the father of the new science of positive psychology, Martin E.P. Seligman draws on more than twenty years of clinical research to demonstrate how optimism enchances the quality of life, and how anyone can learn to practice it. Offering many simple techniques, Dr. Seligman explains how to break an “I—give-up” habit, develop a more constructive explanatory style for interpreting your behavior, and experience the benefits of a more positive interior dialogue. These skills can help break up depression, boost your immune system, better develop your potential, and make you happier..
With generous additional advice on how to encourage optimistic behavior at school, at work and in children, Learned Optimism is both profound and practical–and valuable for every phase of life.

“Range is an urgent and important book, an essential read for bosses, parents, coaches, and anyone who cares about improving performance.” —Daniel H. Pink
What's the most effective path to success in any domain? It's not what you think.
Plenty of experts argue that anyone who wants to develop a skill, play an instrument, or lead their field should start early, focus intensely, and rack up as many hours of deliberate practice as possible. If you dabble or delay, you’ll never catch up to the people who got a head start. But a closer look at research on the world’s top performers, from professional athletes to Nobel laureates, shows that early specialization is the exception, not the rule.
David Epstein examined the world’s most successful athletes, artists, musicians, inventors, forecasters and scientists. He discovered that in most fields—especially those that are complex and unpredictable—generalists, not specialists, are primed to excel. Generalists often find their path late, and they juggle many interests rather than focusing on one. They’re also more creative, more agile, and able to make connections their more specialized peers can’t see.
Provocative, rigorous, and engrossing, Range makes a compelling case for actively cultivating inefficiency. Failing a test is the best way to learn. Frequent quitters end up with the most fulfilling careers. The most impactful inventors cross domains rather than deepening their knowledge in a single area. As experts silo themselves further while computers master more of the skills once reserved for highly focused humans, people who think broadly and embrace diverse experiences and perspectives will increasingly thrive.

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.