Sipser's candid, crystal-clear style allows students at every level to understand and enjoy this field. His innovative "proof idea" sections explain profound concepts in plain English. The new edition incorporates many improvements students and professors have suggested over the years, and offers updated, classroom-tested problem sets at the end of each chapter.

    

    More importantly, this book provides a framework for thinking about computer organization and design that will enable the reader to continue the lifetime of learning necessary for staying at the forefront of this competitive discipline. --John Crawford Intel Fellow Director of Microprocessor Architecture, Intel The performance of software systems is dramatically affected by how well software designers understand the basic hardware technologies at work in a system. Similarly, hardware designers must understand the far reaching effects their design decisions have on software applications. For readers in either category, this classic introduction to the field provides a deep look into the computer. It demonstrates the relationship between the software and hardware and focuses on the foundational concepts that are the basis for current computer design. Using a distinctive learning by evolution approach the authors present each idea from its first principles, guiding readers through a series of worked examples that incrementally add more complex instructions until they ha

    Readable and stimulating, yet well-balanced and in-depth, this text tells the story of evolution, from the history of the study to the most revent developments in evolutionary theory. The third edition of this successful textbook features updates and extensive new coverage. The sections on adaptation and diversity have been reorganized for improved clarity and flow, and a completely updated section on the evolution of sex and the inclusion of more plant examples have all helped to shape this new edition. Evolution also features strong, balanced coverage of population genetics, and scores of new applied plant and animal examples make this edition even more accessible and engaging. Dedicated website - provides an interactive experience of the book, with illustrations downloadable to PowerPoint, and a full supplemental package complementing the book - www.blackwellpublishing.com/ridley. Margin icons - indicate where there is relevant information included in the dedicated website. Two new chapters - one on evolutionary genomics and one on evolution and development bring state-of-the-art information to the coverage of evolutionary study. Two kinds of boxes - one featuring practical applications and the other related information, supply added depth without interrupting the flow of the text. Margin comments - paraphrase and highlight key concepts. Study and review questions - help students review their understanding at the end of each chapter, while new challenge questions prompt students to synthesize the chapter concepts to reinforce the learning at a deeper level.

    Non- mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance-all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code f

    Not to worry: This book is the one place you can turn to find quick, non-intimidating, excellent design help. In The Non-Designer's Design Book, 2nd Edition, best-selling author Robin Williams turns her attention to the basic principles of good design and typography. All you have to do is follow her clearly explained concepts, and you'll begin producing more sophisticated, professional, and interesting pages immediately. Humor-infused, jargon-free prose interspersed with design exercises, quizzes, illustrations, and dozens of examples make learning a snap—which is just what audiences have come to expect from this best-selling author.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    Collected from the series of blog posts starting at: https://bartoszmilewski.com/2014/10/2...Hardcover available at: http://www.blurb.com/b/9008339-catego...

    Requiring only basic algebra and trigonometry, the new edition is notable for its readability, learning features and numerous full-color photos and illustrations. A systems approach is used to cover state-of-the-art communications technologies, to best reflect current industry practice. This edition contains greatly expanded and updated material on the Internet, cell phones, and wireless technologies. Practical skills like testing and troubleshooting are integrated throughout. A brand-new Laboratory & Activities Manual provides both hands-on experiments and a variety of other activities, reflecting the variety of skills now needed by technicians. A new Online Learning Center web site is available, with a wealth of learning resources for students. An Instructor Productivity Center CD-ROM features solutions to all problems, PowerPoint lessons, and ExamView test banks for each chapter.

    KEY TOPICS: The major changes of the revision are reorganization of chapter material and the addition of a much wider range of difficulties.

    *A comprehensive overview of hardware and software issues make this a "must-have" for electrical and computer engineers*Contains new material on RISC processors, performance analysis, multiprocessors and memory systems*New two-color design and illustrations illuminate the text

    Fully revised to meet the needs of the wide range of students beginning engineering courses, this edition has an extended Foundation section including new chapters on graphs, trigonometry, binomial series and functions and a CD-ROM

    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, underscoring the exciting general purpose nature of genetic algorithms as search methods that can be employed across disciplines.An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

    In this updated edition author Bruce Hyslop uses crystal-clear instructions and friendly prose to introduce you to all of today's HTML and CSS essentials. The book has been refreshed to feature current web design best practices. You'll learn how to design, structure, and format your website. You'll learn about the new elements and form input types in HTML5. You'll create and use images, links, styles, and forms; and you'll add video, audio, and other multimedia to your site. You'll learn how to add visual effects with CSS3. You'll understand web standards and learn from code examples that reflect today's best practices. Finally, you will test and debug your site, and publish it to the web. Throughout the book, the author covers all of HTML and offers essential coverage of HTML5 and CSS techniques.

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.