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

    Evolutionary Dynamics is concerned with these equations of life. In this book, Martin A. Nowak draws on the languages of biology and mathematics to outline the mathematical principles according to which life evolves. His work introduces readers to the powerful yet simple laws that govern the evolution of living systems, no matter how complicated they might seem. Evolution has become a mathematical theory, Nowak suggests, and any idea of an evolutionary process or mechanism should be studied in the context of the mathematical equations of evolutionary dynamics. His book presents a range of analytical tools that can be used to this end: fitness landscapes, mutation matrices, genomic sequence space, random drift, quasispecies, replicators, the Prisoner's Dilemma, games in finite and infinite populations, evolutionary graph theory, games on grids, evolutionary kaleidoscopes, fractals, and spatial chaos. Nowak then shows how evolutionary dynamics applies to critical real-world problems, including the progression of viral diseases such as AIDS, the virulence of infectious agents, the unpredictable mutations that lead to cancer, the evolution of altruism, and even the evolution of human language. His book makes a clear and compelling case for understanding every living system--and everything that arises as a consequence of living systems--in terms of evolutionary dynamics.

    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

    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.

    Whittaker defines both in-the-small techniques for individual testers and in-the-large techniques to supercharge test teams. He also introduces a hybrid strategy for injecting exploratory concepts into traditional scripted testing. You'll learn when to use each, and how to use them all successfully.Concise, entertaining, and actionable, this book introduces robust techniques that have been used extensively by real testers on shipping software, illuminating their actual experiences with these techniques, and the results they've achieved. Writing for testers, QA specialists, developers, program managers, and architects alike, Whittaker answers crucial questions such as:- Why do some bugs remain invisible to automated testing--and how can I uncover them?- What techniques will help me consistently discover and eliminate "show stopper" bugs?- How do I make manual testing more effective--and less boring and unpleasant?- What's the most effective high-level test strategy for each project?- Which inputs should I test when I can't test them all?- Which test cases will provide the best feature coverage?- How can I get better results by combining exploratory testing with traditional script or scenario-based testing?- How do I reflect feedback from the development process, such as code changes?

    In the Mastering Emacs ebook you will learn the answers to all the concepts that take weeks, months or even years to truly learn, all in one place.“Emacs is such a hard editor to learn”But why is it so hard to learn? As it turns out, it's almost always the same handful of issues that everyone faces.If you have tried to learn Emacs you will have struggled with the same problems everyone faces, and few tutorials to see you through it.I have dedicated the first half of the book to explaining the essence of Emacs — and in doing so, how to overcome these issues:Memorizing Emacs’s keys: You will learn Emacs one key at a time, starting with the arrow keys. To feel productive in Emacs, it’s important you start on an equal footing — without too many new concepts and keys to memorize. Each chapter will introduce more keys and concepts so you can learn at your own pace. Discovering new modes and features: Emacs is a self-documenting editor, and I will teach you how to use the apropos, info, and describe system to discover new modes and features, or help you find things you forgot! Customizing Emacs: You don’t have to learn Emacs Lisp to alter a lot of Emacs’s functionality. Most changes you want to make are possible using Emacs’s Customize interface and I will show you how to use it efficiently. Understanding the terminology: Emacs is so old it predates almost every other editor and all modern user interfaces. I have an entire chapter dedicated to the unique terminology in Emacs; how it is different from other editors, and what that means to you.

    In order to ensure a high level of knowledge and training, the International Requirements Engineering Board (IREB) worked out the training concept “Certified Professional for Requirements Engineering”, which defines a requirements engineer’s practical skills on different training levels. The book covers the different subjects of the curriculum for the “Certified Professional for Requirements Engineering” (CPRE) defined by the International Requirements Engineering Board (IREB). It supports its readers in preparing for the test to achieve the “Foundation Level” of the CPRE.

    C. R. Licklider, whose visionary dream of a human-computer symbiosis transformed the course of modern science and led to the development of the personal computer. Reprint.

    But while most of us today can work a computer--albeit with the help of the ever-present computer software manual--we know little about what goes on inside the box and virtually nothing about software designor the world of computer programming. In Patterns of Software, the respected software pioneer and computer scientist, Richard Gabriel, gives us an informative inside look at the world of software design and computer programming and the business that surrounds them. In this wide-ranging volume, Gabriel discusses such topics as whatmakes a successful programming language, how the rest of the world looks at and responds to the work of computer scientists, how he first became involved in computer programming and software development, what makes a successful software business, and why his own company, Lucid, failed in 1994, tenyears after its inception. Perhaps the most interesting and enlightening section of the book is Gabriel's detailed look at what he believes are the lessons that can be learned from architect Christopher Alexander, whose books--including the seminal A Pattern Language--have had a profound influence on the computer programmingcommunity. Gabriel illuminates some of Alexander's key insights--the quality without a name, pattern languages, habitability, piecemeal growth--and reveals how these influential architectural ideas apply equally well to the construction of a computer program. Gabriel explains the concept ofhabitability, for example, by comparing a program to a New England farmhouse and the surrounding structures which slowly grow and are modified according to the needs and desires of the people who live and work on the farm. Programs live and grow, and their inhabitants--the programmers--need to workwith that program the way the farmer works with the homestead. Although computer scientists and software entrepreneurs will get much out of this book, the essays are accessible to everyone and will intrigue anyone curious about Silicon Valley, computer programming, or the world of high technology.

    It is also about Python. However, there is much more. The study of algorithms and data structures is central to understanding what computer science is all about. Learning computer science is not unlike learning any other type of difficult subject matter. The only way to be successful is through deliberate and incremental exposure to the fundamental ideas. A beginning computer scientist needs practice so that there is a thorough understanding before continuing on to the more complex parts of the curriculum. In addition, a beginner needs to be given the opportunity to be successful and gain confidence. This textbook is designed to serve as a text for a first course on data structures and algorithms, typically taught as the second course in the computer science curriculum. Even though the second course is considered more advanced than the first course, this book assumes you are beginners at this level. You may still be struggling with some of the basic ideas and skills from a first computer science course and yet be ready to further explore the discipline and continue to practice problem solving. We cover abstract data types and data structures, writing algorithms, and solving problems. We look at a number of data structures and solve classic problems that arise. The tools and techniques that you learn here will be applied over and over as you continue your study of computer science.

    Judy Willis responds with an emphatic yes in this informative guide to getting better results in math class. Tapping into abundant research on how the brain works, Willis presents a practical approach for how we can improve academic results by demonstrating certain behaviors and teaching students in a way that minimizes negativity.With a straightforward and accessible style, Willis shares the knowledge and experience she has gained through her dual careers as a math teacher and a neurologist. In addition to learning basic brain anatomy and function, readers will learn how to* Improve deep-seated negative attitudes toward math.* Plan lessons with the goal of achievable challenge in mind.* Reduce mistake anxiety with techniques such as errorless math and estimation.* Teach to different individual learning strengths and skill levels.* Spark motivation.* Relate math to students' personal interests and goals.* Support students in setting short-term and long-term goals.* Convince students that they can change their intelligence.With dozens of strategies teachers can use right now, Learning to Love Math puts the power of research directly into the hands of educators. A Brain Owner's Manual, which dives deeper into the structure and function of the brain, is also included--providing a clear explanation of how memories are formed and how skills are learned. With informed teachers guiding them, students will discover that they can build a better brain . . . and learn to love math!

    Today, Intel and other semiconductor firms are abandoning the single fast processor model in favor of multi-core microprocessors--chips that combine two or more processors in a single package. In the fourth edition of "Computer Architecture," the authors focus on this historic shift, increasing their coverage of multiprocessors and exploring the most effective ways of achieving parallelism as the key to unlocking the power of multiple processor architectures. Additionally, the new edition has expanded and updated coverage of design topics beyond processor performance, including power, reliability, availability, and dependability. CD System Requirements"PDF Viewer"The CD material includes PDF documents that you can read with a PDF viewer such as Adobe, Acrobat or Adobe Reader. Recent versions of Adobe Reader for some platforms are included on the CD. "HTML Browser"The navigation framework on this CD is delivered in HTML and JavaScript. It is recommended that you install the latest version of your favorite HTML browser to view this CD. The content has been verified under Windows XP with the following browsers: Internet Explorer 6.0, Firefox 1.5; under Mac OS X (Panther) with the following browsers: Internet Explorer 5.2, Firefox 1.0.6, Safari 1.3; and under Mandriva Linux 2006 with the following browsers: Firefox 1.0.6, Konqueror 3.4.2, Mozilla 1.7.11. The content is designed to be viewed in a browser window that is at least 720 pixels wide. You may find the content does not display well if your display is not set to at least 1024x768 pixel resolution. "Operating System"This CD can be used under any operating system that includes an HTML browser and a PDF viewer. This includes Windows, Mac OS, and most Linux and Unix systems. Increased coverage on achieving parallelism with multiprocessors. Case studies of latest technology from industry including the Sun Niagara Multiprocessor, AMD Opteron, and Pentium 4. Three review appendices, included in the printed volume, review the basic and intermediate principles the main text relies upon. Eight reference appendices, collected on the CD, cover a range of topics including specific architectures, embedded systems, application specific processors--some guest authored by subject experts.

    "Explaining Physics" emphasizes the applications and social effects of physics, and extends its treatment of energy and electronics. The features are: introductory page to each unit to bring out the relevance of the material to everyday life; simple questions at the end of each unit to consolidate learning; and helpful revision summary.

    

    This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results