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

    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.

    For anyone who has ever wondered how computers solve problems, an engagingly written guide for nonexperts to the basics of computer algorithms.

    Floyd's acclaimed emphasis on "applications using real devices" and on "troubleshooting" gives users the problem-solving experience they'll need in their professional careers. Known for its clear, accurate explanations of theory supported by superior exercises and examples, this book's full-color format is packed with the visual aids today's learners need to grasp often complex concepts. KEY TOPICS The book features a comprehensive review of fundamental topics and a unique introduction to two popular programmable logic software packages (Altera and Xilinx) and boundary scan software. For electronic technicians, system designers, engineers.

    Throughout the writing process of this edition, the authors worked closely with a developmental editor to help ensure the text is student-friendly and even more responsive to the variety of learning styles. The text is enriched with real-world examples and illustrations from a variety of business environments. Futhermore, the text's new, four-color design allows instructors a high degree of flexibility and emphasis.

    Wolfram lets the world see his work in A New Kind of Science, a gorgeous, 1,280-page tome more than a decade in the making. With patience, insight, and self-confidence to spare, Wolfram outlines a fundamental new way of modeling complex systems. On the frontier of complexity science since he was a boy, Wolfram is a champion of cellular automata--256 "programs" governed by simple nonmathematical rules. He points out that even the most complex equations fail to accurately model biological systems, but the simplest cellular automata can produce results straight out of nature--tree branches, stream eddies, and leopard spots, for instance. The graphics in A New Kind of Science show striking resemblance to the patterns we see in nature every day. Wolfram wrote the book in a distinct style meant to make it easy to read, even for nontechies; a basic familiarity with logic is helpful but not essential. Readers will find themselves swept away by the elegant simplicity of Wolfram's ideas and the accidental artistry of the cellular automaton models. Whether or not Wolfram's revolution ultimately gives us the keys to the universe, his new science is absolutely awe-inspiring. --Therese Littleton

    It covers the basics of computer programming, including variables and values, functions, conditionals and control flow, program development and debugging. Later chapters cover basic algorithms and data structures.

    This is type of problems asked at the JEE (IIT). The purpose of this book is to show students how to handle such problems and give them sufficient practice in solving problems of this type, thus building their confidence. The main features of this book are:Each chapter begins with a summary of facts, formulate and working techniques. Trick, tips and techniques have been clearly marked with the icon.A large number of problems have been solved and explained in each chapter.The exercises contain short-answer, long-answer and objective type questions.Multiple-choice questions in which more than one option may be correct have also been given.Time-bound tests at the end of each chapter will help students practise answering questions in a given time.The book also includes integrated tests, bases on all the chapters.A chapter containing miscellaneous problems has been given at the end of the book. This will help students gain confidence in solving problems without prior knowledge of the chapter(s) to which the problems belong.Table of ContentsAlgebraProgressions, Related Inequalities and SeriesDeterminants and Cramer's RuleEquations, Inequations and ExpressionsComplex NumbersPermutation and CombinationBinomial Theorem for Positive Integral IndexPrinciple of Mathematical Induction (PMI)Infinite SeriesMatricesTrigonometryCircular Functions, IdentitiesSolution of EquationsInverse Circular FunctionsTrigonometrical Inequalities and InequationsLogarithmProperties of TriangleHeights and DistancesCoordinate GeometryCoordinates and Straight LinesPairs of Straight Lines and Transformation of AxesCirclesParabolaEllipse and HyperbolaCalculusFunctionDifferentiationLimit, Indeterminate FormContinuity, Differentiability and Graph of FunctionApplication of dy/dxMaxima and MinimaMonotonic Function and Lagrange's TheoremIndefinite In

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    It provides many detailed, practical design and analysis examples intended to relate theory to the workplace. Chapter topics include first experiences with an op amp; inverting and noninverting amplifiers; comparators and controls; selected applications of op amps; signal generators; op amps with diodes; differential, instrumentation, and bridge amplifiers; DC performance: bias, offsets, and drift; AC performance: bandwidth, slew rate, noise; active filters; modulating, demodulating, and frequency changing with the multiplier; integrated-circuit timers; digital-to-analog converters; analog-to-digital converters; and power supplies. For design engineers rs

    Numerous examples throughout provide readers with real-life applications of the concepts of analog and digital communications systems, while chapter-end questions and problems give them a chance to test and review their understanding of fundamental and key topics. Modern digital and data communications systems, microwave radio communications systems, satellite communications systems, and optical fiber communications systems. Cellular and PCS telephone systems coverage presents the latest and most innovative technological advancements being made in cellular communication systems. Optical fiber communications chapter includes new sections on light sources, optical power, optical sources and link budget. Current topics include trellis encoding, CCITT modem recommendations, PCM line speed, extended superframe format, wavelength division multiplexing, Kepler's laws, Clark orbits, limits of visibility, Satellite Radio Navigation and Navstar GPS. For the study of electronic communications systems.

    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.

    Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.