The book is non-theoretical, explaining concepts intuitively and teaching problem solving through worked examples and step-by-step instructions. This edition places more emphasis on conceptual understanding and understanding results. This edition also features increased emphasis on Excel, MINITAB, and the TI-83 Plus and TI 84-Plus graphing calculators, computing technologies commonly used in such courses.

    In this cookbook, prolific hacker and author Simon Monk provides more than 200 practical recipes for running this tiny low-cost computer with Linux, programming it with Python, and hooking up sensors, motors, and other hardware—including Arduino.You’ll also learn basic principles to help you use new technologies with Raspberry Pi as its ecosystem develops. Python and other code examples from the book are available on GitHub. This cookbook is ideal for programmers and hobbyists familiar with the Pi through resources such as Getting Started with Raspberry Pi (O’Reilly).Set up and manage your Raspberry PiConnect the Pi to a networkWork with its Linux-based operating systemUse the Pi’s ready-made softwareProgram Raspberry Pi with PythonControl hardware through the GPIO connectorUse Raspberry Pi to run different types of motorsWork with switches, keypads, and other digital inputsHook up sensors for taking various measurementsAttach different displays, such as an LED matrixCreate dynamic projects with Raspberry Pi and Arduino Make sure to check out 10 of the over 60 video recipes for this book at: http://razzpisampler.oreilly.com/ You can purchase all recipes at:

    The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks.The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.Subjects covered include the measurement and structure of networks in many branches of science, methods for analyzing network data, including methods developed in physics, statistics, and sociology, the fundamentals of graph theory, computer algorithms, and spectral methods, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks.

    The author successfully introduces Maxwell's equations, wave propagation, network analysis, and design principles as applied to modern microwave engineering. A considerable amount of material in this book is related to the design of specific microwave circuits and components, for both practical and motivational value. It also presents the analysis and logic behind these designs so that the reader can see and understand the process of applying the fundamental concepts to arrive at useful results. The derivations are well laid out and the majority of each chapter's formulas are displayed in a nice tabular format every few pages. This Third Edition offers greatly expanded coverage with new material on: Noise; Nonlinear effects; RF MEMs; transistor power amplifiers; FET mixers; oscillator phase noise; transistor oscillators and frequency multiplier.

    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.

    But building a machine learning system requires that you make practical decisions: Should you collect more training data? Should you use end-to-end deep learning? How do you deal with your training set not matching your test set? and many more. Historically, the only way to learn how to make these "strategy" decisions has been a multi-year apprenticeship in a graduate program or company. This is a book to help you quickly gain this skill, so that you can become better at building AI systems.

    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.

    This text is user-focused and has been highly updated including topics, pictures and examples. The Williams text contains less theory and more application to engage students who might be more familiar with technology. Continually published and updated for over 15 years, Using Information Technology was the first text to foresee and define the impact of digital convergence--the fusion of computers and communications. It was also the first text to acknowledge the new priorities imposed by the Internet and World Wide Web and bring discussion of them from late in the course to the beginning. Today, it is directed toward the "Always On" generation that is at ease with digital technology--comfortable with iPhones, MySpace, Facebook, Twitter, Wikipedia, and the blogosphere--but not always savvy about its processes, possibilities, and liabilities. This 8th edition continues to address the two most significant challenges that instructors face in teaching this course: -Trying to make the course interesting and challenging, and -Trying to teach to students with a variety of computer backgrounds. In addition, this text correlates with Simnet Online for full integration of resources within the Computing Concepts course.

    For examples see chapters 3, 4, 5 and 9. * Mechanical property coverage The Sixth Edition maintains its extensive, introductory level coverage of mechanical properties and failure--the most important materials considerations for many engineers. For examples see chapters 6, 7, & 8. * A picture is worth 1000 words! The Sixth Edition judiciously and extensively makes use of illustrations and photographs. The approximate 500 figures include a large number of photographs that show the microstructure of various materials (e.g., Figures 9.12, 10.8, 13.12, 14.15 and 16.5). * Current and up-to-date Students are presented with the latest developments in Material Science and Engineering. Such up-to-date content includes advanced ceramic and polymeric materials, composites, high-energy hard magnetic materials, and optical fibers in communications. For examples see sections 13.7, 15.19, 16.8, 20.9, and 21.14. * Why study These sections at the beginning of each chapter provide the student with reasons why it is important to learn the material covered in the chapter. * Learning objectives A brief list of learning objectives for each chapter states the key learning concepts for the chapter. * Resources to facilitate the materials selection process. Appendix B, which contains 11 properties for a set of approximately 100 materials, is included which be used in materials selection problems. An additional resource, Appendix C, contains the prices for all materials listed in Appendix B. * The text is packaged with a CD-ROM that contains 1) interactive software modules to enhance visualization of three-dimensional objects, 2) additional coverage of select topics, and 3) complete solutions to selected problems from the text in order to assist students in mastering problem-solving.

    Linear Equations; Vector Spaces; Linear Transformations; Polynomials; Determinants; Elementary canonical Forms; Rational and Jordan Forms; Inner Product Spaces; Operators on Inner Product Spaces; Bilinear Forms For all readers interested in linear algebra.

    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.

    Getting Started with Arduino gives you lots of ideas for Arduino projects and helps you get going on them right away. From getting organized to putting the final touches on your prototype, all the information you need is right in the book. Inside, you'll learn about:Interaction design and physical computing The Arduino hardware and software development environment Basics of electricity and electronics Prototyping on a solderless breadboard Drawing a schematic diagram And more. With inexpensive hardware and open-source software components that you can download free, getting started with Arduino is a snap. To use the introductory examples in this book, all you need is a USB Arduino, USB A-B cable, and an LED. Join the tens of thousands of hobbyists who have discovered this incredible (and educational) platform. Written by the co-founder of the Arduino project, with illustrations by Elisa Canducci, Getting Started with Arduino gets you in on the fun! This 128-page book is a greatly expanded follow-up to the author's original short PDF that's available on the Arduino website.

    

    Our inaugural effort is OpenIntro Statistics. Probability is optional, inference is key, and we feature real data whenever possible. Files for the entire book are freely available at openintro.org, and anybody can purchase a paperback copy from amazon.com for under $10.The future for OpenIntro depends on the involvement and enthusiasm of our community. Visit our website, openintro.org. We provide free course management tools, including an online question bank, utilities for creating course quizzes, and many other helpful resources.CERTAIN CONTENT THAT APPEARS ON THIS SITE COMES FROM AMAZON SERVICES LLC. THIS CONTENT IS PROVIDED ‘AS IS’ AND IS SUBJECT TO CHANGE OR REMOVAL AT ANY TIME.Can’t find it here? Search Amazon.com Search: All Products Apparel & AccessoriesBabyBeautyBooksCamera & PhotoCell Phones & ServiceClassical MusicComputersComputer & Video GamesDVDElectronicsGourmet FoodHome & GardenMiscellaneousHealth & Personal CareJewelry & WatchesKitchen & HousewaresMagazine SubscriptionsMusicMusical InstrumentsSoftwareSports & OutdoorsTools & HardwareToys & GamesVHS Keywords:

     At any given moment, somewhere in the world someone struggles with the same software design problems you have. You know you don't want to reinvent the wheel (or worse, a flat tire), so you look to Design Patterns--the lessons learned by those who've faced the same problems. With Design Patterns, you get to take advantage of the best practices and experience of others, so that you can spend your time on...something else. Something more challenging. Something more complex. Something more fun. You want to learn about the patterns that matter--why to use them, when to use them, how to use them (and when NOT to use them). But you don't just want to see how patterns look in a book, you want to know how they look "in the wild". In their native environment. In other words, in real world applications. You also want to learn how patterns are used in the Java API, and how to exploit Java's built-in pattern support in your own code. You want to learn the real OO design principles and why everything your boss told you about inheritance might be wrong (and what to do instead). You want to learn how those principles will help the next time you're up a creek without a design pattern. Most importantly, you want to learn the "secret language" of Design Patterns so that you can hold your own with your co-worker (and impress cocktail party guests) when he casually mentions his stunningly clever use of Command, Facade, Proxy, and Factory in between sips of a martini. You'll easily counter with your deep understanding of why Singleton isn't as simple as it sounds, how the Factory is so often misunderstood, or on the real relationship between Decorator, Facade and Adapter. With Head First Design Patterns, you'll avoid the embarrassment of thinking Decorator is something from the "Trading Spaces" show. Best of all, in a way that won't put you to sleep! We think your time is too important (and too short) to spend it struggling with academic texts. If you've read a Head First book, you know what to expect--a visually rich format designed for the way your brain works. Using the latest research in neurobiology, cognitive science, and learning theory, Head First Design Patterns will load patterns into your brain in a way that sticks. In a way that lets you put them to work immediately. In a way that makes you better at solving software design problems, and better at speaking the language of patterns with others on your team.