It describes conventional and unconventional methods of electricity generation and its economics, distribution methods, substation location, electric drives, high frequency power for induction and heating, illumination engineering, and electric traction. Each chapter contains illustrative worked problems, exercises (some with answers) and a bibliography.

     Offers extensively updated coverage, new problem sets, and chapter-ending material to enhance the book’s relevance to today’s engineers and scientists. Includes new problem sets demonstrating updated applications to engineering as well as biological, physical, and computer science. Emphasizes key ideas as well as the risks and hazards associated with practical application of the material. Includes new material on topics including: difference between discrete and continuous measurements; binary data; quartiles; importance of experimental design; “dummy” variables; rules for expectations and variances of linear functions; Poisson distribution; Weibull and lognormal distributions; central limit theorem, and data plotting. Introduces Bayesian statistics, including its applications to many fields. For those interested in learning more about probability and statistics.

    Python is considered easy to learn, but there's no quicker way to mastery of the language than learning from an expert teacher. This edition of "Learning Python" puts you in the hands of two expert teachers, Mark Lutz and David Ascher, whose friendly, well-structured prose has guided many a programmer to proficiency with the language. "Learning Python," Second Edition, offers programmers a comprehensive learning tool for Python and object-oriented programming. Thoroughly updated for the numerous language and class presentation changes that have taken place since the release of the first edition in 1999, this guide introduces the basic elements of the latest release of Python 2.3 and covers new features, such as list comprehensions, nested scopes, and iterators/generators. Beyond language features, this edition of "Learning Python" also includes new context for less-experienced programmers, including fresh overviews of object-oriented programming and dynamic typing, new discussions of program launch and configuration options, new coverage of documentation sources, and more. There are also new use cases throughout to make the application of language features more concrete. The first part of "Learning Python" gives programmers all the information they'll need to understand and construct programs in the Python language, including types, operators, statements, classes, functions, modules and exceptions. The authors then present more advanced material, showing how Python performs common tasks by offering real applications and the libraries available for those applications. Each chapter ends with a series of exercises that will test your Python skills and measure your understanding."Learning Python," Second Edition is a self-paced book that allows readers to focus on the core Python language in depth. As you work through the book, you'll gain a deep and complete understanding of the Python language that will help you to understand the larger application-level examples that you'll encounter on your own. If you're interested in learning Python--and want to do so quickly and efficiently--then "Learning Python," Second Edition is your best choice.

    But Neil Armstrong and Sally Ride would have never made history, and humankind would not have touched the stars, if not for the men and women on the ground who lit the fuse that launched the first rockets.Enthralled as a boy by the exploits of Flash Gordon and the novels of Robert Heinlein and Arthur C. Clarke—who put the science in science fiction—James French became one of the original unsung engineers of America’s groundbreaking space program. His fascinating memoir offers an up-close-and-technical look at building, testing, and perfecting the pioneering Saturn rockets and original lunar landing module, and he shares true tales, both humorous and harrowing, of life—and near death—on the front lines of scientific exploration.If you’ve ever said, “It’s not rocket science,” you’re right. It’s rocket engineering—and here’s your chance to marvel at how it changed the world and made it possible to explore all that lies beyond Earth. James R French graduated from MIT in 1958 with a degree of BSME Specializing in Propulsion. His first job was with Rocketdyne Division of North American Aviation where he worked on developmental testing of H-1 engines and combustion devices hardware for F-1 and J-2 engines used in Saturn 5. Mr. French has also worked at TRW Systems, where he was Lead Development Test Engineer on the Lunar Module Descent Engine, and Jet Propulsion Laboratory where he was Advanced Planetary studies Manager as well as Chief Engineer for the SP-100 Space Nuclear Power System and worked on Mariners 5, 6, 7, 8, and 9; Viking 1 & 2 and Voyager 1 & 2. . In 1986, he helped found American Rocket Co., a commercial launch company.Since 1987, Mr. French has been consultant to a variety of aerospace companies, SDIO, NASA, and USAF. He has participated in various startup companies in the private space flight arena and currently consults extensively to Blue Origin. Mr. French is co-author with Dr. Michael Griffin of the best-selling text Space Vehicle Design, published by AIAA. The second edition of the book has received the Summerfield Book Award for 2008. Mr. French is a Fellow of both AIAA and the British Interplanetary Society and a 50+ year member of AIAA. He has held several Technical Committee and other posts in AIAA. Cover design by Evan Twohy

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    For courses in computer organization and architecture, this text provides a clear, comprehensive presentation of the organization and architecture of contemporary computers.

    This well-established two-book course is designed for class teaching and private study leading to GCSE examinations in mathematics and further Mathematics at A Level.

    Written for the engineer as well as the race car enthusiast, the authors, who developed many of the original vehicle dynamics theories and principles covered in this book, including the Moment Method, pair analysis and lap time simulation, include much information that is not available in any other vehicle dynamics text.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    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.

    Using this simple device, you can: • Increase Strength • Improve Balance • Reduce Lower Back Pain • Rehabilitate Injuries Resistance Band Workbook offers more than 70 safe, effective exercises paired with clear captions and step- by-step photos that can be done practically anywhere, anytime. In addition, special programs will enhance your daily life, whether you’re looking to revamp your physique, elevate your sports performance or simply improve functional fitness.

    This comprehensive, illustrated book gives essential advice on how to become the pack leader by understanding and interpreting the body language of man's best friend. Topics include choosing your Border Collie, taking your puppy home, common sense dog training, nutrition, creating a partnership, and adolescent and older dogs.

    It is the only dictionary that provides concise, accurate definitions illustrated with finely detailed, hand-rendered drawings, each executed in Mr. Ching's signature style.

     This top-selling, theorem-proof text presents a careful treatment of the principal topics of linear algebra, and illustrates the power of the subject through a variety of applications. It emphasizes the symbiotic relationship between linear transformations and matrices, but states theorems in the more general infinite-dimensional case where appropriate.

    Updated to cover the new Transportation and Structural codes. As the most comprehensive reference and study guide available for engineers preparing for the morning and afternoon topics on the Civil PE exam, the 11th edition of the Civil Engineering Reference Manual provides a concentrated review of the exam topics. No other exam-focused publication is more complete. The Most Trusted Reference for the Civil PE Exam is also the Most Up-To-Date A complete introduction to the exam format and content A suggested study schedule, plus tips for successful exam preparation Nearly 500 solved example problems Hundreds of key tables, charts, and figures at your fingertips An easy-to-use index and full glossary for quick reference Perfect for post-exam reference What's New in the 11th Edition 4 new construction engineering chapters Over 35 updated chapters—including extensively revised structural and transportation chapters 100 new equations Over 300 new, easy-to-use index entriesExam Topics CoveredConstruction—NewGeotechnical—UpdatedStructural—UpdatedTransportation—UpdatedWater Resources & Environmental—Updated_____________________________Since 1975 more than 2 million people preparing for their engineering, surveying, architecture, LEED®, interior design, and landscape architecture exams have entrusted their exam prep to PPI. For more information, visit us at www.ppi2pass.com.