Several things make this one stand out. Here's the most important: Linux Administration Handbook is designed for administrators working in industrial-strength production environments. It never glosses over the "subtleties" that can get you in big trouble. It doesn't stint on technical detail. It's never satisfied with restating the man pages. And it's full of war stories from folks who've been there. Evi Nemeth and her coauthors: Boy, have they ever been there. (Just ask any gray-bearded Unix sysadmin about their earlier, legendary Unix System Administration Handbook.) There's only been one downside to Linux Administration Handbook: It's been nearly five years since it was written. Well, that flaw's just been remedied. The new Second Edition has been systematically revised for the latest administration tools (think Nagios and LVM). It's carefully targeted at today's five most widely used distributions: Red Hat Enterprise Linux 4.3, Fedora Core 5, SUSE Linux 10.2, Debian 3.2 "Etch," and Ubuntu 6.06. The result: a book you can rely on for the next five years. Rely on to do what? Just about everything. You'll find chapters on booting and shutting down; "rootly" powers; controlling processes; the Linux filesystem; on adding new users. You'll learn the most efficient ways to perform backups. How to make sense of syslogs and log files. Everything you need to know about drivers, the kernel, networking, NFS -- and Internet services, from web hosting to email. Nemeth & Company bring their experience to bear on troubleshooting, performance optimization, print management, security, Windows interoperability, even "policies and politics." Whatever Linux books you already own, if you depend on Linux to run efficiently and reliably, you need this one, too. Bill Camarda, from the December 2006 href="http://www.barnesandnoble.com/newslet... Only

    Psychology is the only branch of learning in which I am particularly versed. To the psychologist the religious propensities of man must be at least as interesting as any other of the facts pertaining to his mental constitution. It would seem, therefore, as a psychologist, the natural thing for me would be to invite you to a descriptive survey of those religious propensities." When William James went to the University of Edinburgh in 1901 to deliver a series of lectures on "natural religion," he defined religion as "the feelings, acts, and experiences of individual men in their solitude, so far as they apprehend themselves to stand in relation to whatever they may consider the divine." Considering religion, then, not as it is defined by--or takes place in--the churches, but as it is felt in everyday life, he undertook a project that, upon completion, stands not only as one of the most important texts on psychology ever written, not only as a vitally serious contemplation of spirituality, but for many critics one of the best works of nonfiction written in the 20th century. Reading The Varieties of Religious Experience, it is easy to see why. Applying his analytic clarity to religious accounts from a variety of sources, James elaborates a pluralistic framework in which "the divine can mean no single quality, it must mean a group of qualities, by being champions of which in alternation, different men may all find worthy missions." It's an intellectual call for serious religious tolerance--indeed, respect--the vitality of which has not diminished through the subsequent decades.

    No one philosopher contributed more to this enterprise than Kant, whose Critique of Pure Reason (1781) shook the very foundations of the intellectual world. Kant argued that the basic principles of the natural sciences are imposed on reality by human sensibility and understanding, and thus that human beings are also free to impose their own free and rational agency on the world. This volume is the only systematic and comprehensive account of the full range of Kant's writings available, and the first major overview of his work to be published in more than a dozen years. An internationally recognized team of Kant scholars explore Kant's conceptual revolution in epistemology, metaphysics, philosophy of science, moral and political philosophy, aesthetics, and the philosophy of religion. The volume also traces the historical origins and consequences of Kant's work.

    David Peat explains the development and meaning of this Superstring Theory in a thoroughly readable, dramatic manner accessible to lay readers with no knowledge of mathematics. The consequences of the Superstring Theory are nothing less than astonishing.

    The book represents the first philosophically sound discussion of the concept of number in Western civilization. It profoundly influenced developments in the philosophy of mathematics and in general ontology.

    Its full-colour illustration program includes many novel, visually effective features to help students learn.

    In this pocket version of his bestselling Life Without Diabetes, Professor Roy Taylor offers a brilliantly concise explanation of what happens to us when we get type 2 and how we can escape it.Taylor's research has demonstrated that type 2 is caused by just one factor - too much internal fat in the liver and pancreas - and that to reverse it you need to strip this harmful internal fat out with rapid weight loss.In simple, accessible language, Taylor takes you through the three steps of his clinically proven Newcastle weight loss plan and shows how to incorporate the programme into your life.Complete with FAQs and inspirational tips from his trial participants, this is an essential read for anyone who has been given a diagnosis of type 2 diabetes or pre-diabetes and wants to understand their condition and transform their outcomes.

    Its step-by-step approach guides you through the process, helping you formulate a nursing diagnosis based on known information and assessment findings; identify the appropriate nursing diagnosis; and create a care plan that includes desired outcomes, interventions, and evidence-based rationales. Nursing Diagnosis Handbook is an essential care planning resource you will turn to again and again throughout your nursing education and career.Provides care plans for every NANDA-I approved nursing diagnosis.Includes examples of and suggested NIC and NOC interventions and outcomes for each care plan.A convenient A-to-Z organization in Sections I and II helps you quickly locate key information.Evidence-based practice information is incorporated throughout.Includes complete coverage of pediatric, geriatric, and multicultural considerations, as well as home care and client/family teaching guidelines for each condition.A Care Plan Constructor on the Evolve website helps you create customized plans of care.Features the most up-to-date 2007-2008 NANDA-I approved nursing diagnoses, including approximately 15 new, 20 revised, and 5 replacement diagnoses.Provides a more detailed explanation of NIC and NOC taxonomies and their use in care planning.Explanations of assessment versus action interventions help guide you to the correct choice of intervention.Covers important information on concept mapping.Patient/Family Teaching sections offer expanded wellness and health promotion information.Clustered wellness nursing diagnoses are quick and easy to locate.Includes the latest evidence-based nursing rationales.

    Worms are the latest (as well as, of course, perhaps the oldest!) trend in earth-friendly gardening, and in this handy guide, the authors of DEAD SNAILS LEAVE NO TRAILS demystify the world of worm wrangling, with everything you need to know to build your own worm bin, make your garden worm-friendly, pamper your soil, and much much more.

    Purchasers are entitled to a free trial membership in the General Books Club where they can select from more than a million books without charge. Subjects: Astronomy; History / General; Juvenile Nonfiction / Science

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    Volume 1 looks at the discipline's origins in Babylon and Egypt, the creation of geometry and trigonometry by the Greeks, and the role of mathematics in the medieval and early modern periods. Volume 2 focuses on calculus, the rise of analysis in the 19th century, and the number theories of Dedekind and Dirichlet. The concluding volume covers the revival of projective geometry, the emergence of abstract algebra, the beginnings of topology, and the influence of Godel on recent mathematical study.

    Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. "Introduction to Machine Learning" is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms are explained so that the student can easily move from the equations in the book to a computer program. The book can be used by advanced undergraduates and graduate students who have completed courses in computer programming, probability, calculus, and linear algebra. It will also be of interest to engineers in the field who are concerned with the application of machine learning methods.After an introduction that defines machine learning and gives examples of machine learning applications, the book covers supervised learning, Bayesian decision theory, parametric methods, multivariate methods, dimensionality reduction, clustering, nonparametric methods, decision trees, linear discrimination, multilayer perceptrons, local models, hidden Markov models, assessing and comparing classification algorithms, combining multiple learners, and reinforcement learning.

    The remaining lectures build on that idea, examining the possibility of building a relativistic quantum theory on curved surfaces or flat surfaces.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.