Think big. Think the unthinkable!' As you'll discover in his incomparable memoir, inventor, mechanic, TV presenter and walking tall as the definition of the British eccentric, Edd China sees things differently.An unstoppable enthusiast from an early age, Edd had 35 ongoing car projects while he was at university, not counting the double-decker bus he was living in. Now he's a man with not only a runaround sofa, but also a road-legal office, shed, bed and bathroom. His first car was a more conventional 1303 Texas yellow Beetle, the start of an ongoing love affair with VW, even though it got him arrested for attempted armed robbery.A human volcano of ideas and the ingenuity to make them happen, Edd is exhilarating company. Join him on his wild, wheeled adventures; see inside his engineering heroics; go behind the scenes on Wheeler Dealers.Climb aboard his giant motorised shopping trolley, and let him take you into his parallel universe of possibility.

     This new edition has an earlier evolution of object concepts, developed in a way that capitalizes on the power of objects without overwhelming beginning programmers. It includes all new Java 5 topics, including Scanner class, enumerated types, autoboxing, variable length parameter lists, the enhanced for loop, and generic types. This is in depth coverage on GUI applications. This book is appropriate for beginning programmers who want to learn to program with Java as well as experienced programmers who want to add Java to their skill-set.

    The risk to his friends at the academy is the hidden price. His challenge is how to balance the risks.The final stage of his training is a shakedown cruise on an old minesweeper, barely able to achieve FTL entry and exit.Midway through their cruise, the minesweeper collides with an ancient alien wreck, ripping the sides off the starship. Jack’s team of bots help seal the combined wreck to prevent air loss. Ghost, a nanolife form from the alien wreck, provides assistance.Jack and his girlfriend, Sofia, are the only survivors, and air, food, and supplies are critical.The countdown of days to survive is ticking.No wonder the Royal Family are angry.And there’s still a price on Jack’s head.

    Railway Transpor tations and its Development 2. Railway Terminology 3. Railway Track 4. Stresses in Railway Track 5. Traction and Tractive Resistances 6. Rails 7. Rail Joints and Welding of Rails 8. Creep of Rails 9. Sleepers 10. Track Fittings and Fastenings 11. Ballast 12. Subgrade and Embankments 13. Track Alignments 14. Surveying 15. Geometric Design of the Track 16. Points and Crossings 17. Track Junctions 18. Stations and Yards 19. Equipment in Station Yards 20. Signaling and Control Systems 21. Interlocking of Signals and Points 22. Construction and Renewal of Track 23. Track Drainage 24. Conventional Maintenance of Track (or Manual Maintenance) 25. Railway Track Standards 26. Safety in Railways 27. Underground Railways and Tunnelling. PART- II MODERNIZATION OF RAILWAY TRACK AND FUTURE TRENDS 28. Modern Developments in Railways 29. Development of High and Super High Speeds 30. Modernization of Track for High Speeds 31. Modern Methods of Track Maintenance PART- III RAILWAY ADMINISTRATION, ECONOMICS AND FINANCE 32. Administration of Indian Railways 33. Railway Expenses, Rates and Fares 34. Material Management.

    All of the fundamentals of robotics are covered--robotics analysis; including kinematics, kinetics and force control, and trajectory planning of robots; its sub-systems such as actuators, sensors, and vision systems; as well as robotics applications. "Introduction to Robotics" also includes many subjects related to mechatronics, microprocessor actuator control, integration of sensors, vision systems, and fuzzy logic. For practicing mechanical engineers, electronic and electric engineers, computer engineers, and engineering technologists who would like to learn about robotics.

    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.

    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.

    In this lively book of essays, Stephen Wolfram takes the reader along on some of his most surprising and engaging intellectual adventures in science, technology, artificial intelligence and language design.

    Miranda Summerlin finds that she has been given nanites that give her enhanced mental abilities and extraordinary hand-to-eye coordination. She attends the nearby Space Academy where she uses her mental genius to work on cutting edge astrophysics and she joins the school shooting competition team after discovering she can hit wherever she aims at on a target. Intending to keep her skills low key, Miranda must step up to prevent a terrorist attack against the school that only she has the skills to do so. Miranda has also drawn the attention of a girl on the soccer team who becomes her nemesis at the school. While playing pool at a school party, Miranda lets her irritation with her nemesis cause her to let some of her skills slip thru to others. But Miranda will soon be faced with the need to use many of her skills and in so doing, Miranda will come to the attention of her true nemesis, a rogue Organization that wants her nanites. She will need help to escape from their agent. In the process, she will discover her true identity. But what is the cost for her rescue? How will she deal with the Organization? >>>The first in the Near Future series, Awakening is a near future sci-fi thriller in a world that combines science that just might be possible soon with psychic visions and an evil Organization that is backed by an unknown power. Coming soon, books 2 and 3 in the series and a novella too! Sign up for my Reader's List at http://randalsloan.com/nearfuture/

    Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations. This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation. Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner's perspective, and extensive lists of exercises and class projects. The book's Web site, www.probabilistic-robotics.org, has additional material. The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

     They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk. Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact. This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own machine learning algorithms in Python, this book is for you.

    It is a ‘ must’ book for those in every branch of science . . . in addition, economists, politicians, statesmen, and businessmen cannot afford to overlook cybernetics and its tremendous, even terrifying implications. "It is a beautifully written book, lucid, direct, and despite its complexity, as readable by the layman as the trained scientist." -- John B. Thurston, "The Saturday Review of Literature" Acclaimed one of the "seminal books . . . comparable in ultimate importance to . . . Galileo or Malthus or Rousseau or Mill," "Cybernetics" was judged by twenty-seven historians, economists, educators, and philosophers to be one of those books published during the "past four decades", which may have a substantial impact on public thought and action in the years ahead." -- Saturday Review

    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.

    This proven and accessible text speaks to beginning engineering and math students through a wealth of pedagogical aids, including an abundance of examples, explanations, "Remarks" boxes, definitions, and group projects. Using a straightforward, readable, and helpful style, this book provides a thorough treatment of boundary-value problems and partial differential equations.