He frames the lectures with four fundamental questions: What do we know when we are able to speak and understand a language? How is this knowledge acquired? How do we use this knowledge? What are the physical mechanisms involved in the representation, acquisition, and use of this knowledge? Starting from basic concepts, Chomsky sketches the present state of our answers to these questions and offers prospects for future research. Much of the discussion revolves around our understanding of basic human nature (that we are unique in being able to produce a rich, highly articulated, and complex language on the basis of quite rudimentary data), and it is here that Chomsky's ideas on language relate to his ideas on politics.The initial versions of these lectures were given at the Universidad Centroamericana in Managua, Nicaragua, in March 1986. A parallel set of lectures on contemporary political issues given at the same time has been published by South End Press under the title On Power and Ideology: The Managua Lectures.Language and Problems of Knowledge is sixteenth in the series Current Studies in Linguistics, edited by Jay Keyser.

    In addition to general advice on basic numeracy, the guide points out common errors and explains the recognized techniques for solving financial problems, analysing information of any kind, and effective decision making. Over one hundred charts, graphs, tables, and feature boxes highlight key points. Also included is an A-Z dictionary of terms covering everything from amortization to zero-sum game. Whatever your business, The Economist Numbers Guide will prove invaluable.

    Today, no collection of signals or observations—even from satellites, which can “see” the whole planet with a single instrument—becomes global in time and space without passing through a series of data models. Everything we know about the world's climate we know through models. Edwards offers an engaging and innovative history of how scientists learned to understand the atmosphere—to measure it, trace its past, and model its future.

    The language concepts are aptly explained in simple and easy-to-understand style, supported with examples, illustrations and programming and debugging exercises.

    Hemostasis and coagulation theory, testing, and instrumentation are also thoroughly discussed. Beautiful full-color illustrations throughout the text enhance comprehension and allow students to realistically visualize hematology concepts.Detailed, full-color illustrations appear near hematology concept discussions to visually support comprehension and recognition.Opening Case Studies present real-life scenarios to apply concepts presented in each chapter.Learning Objectives and Review Questions work together to list expected outcomes and then test those concepts for each chapter.A Bulleted Summary highlights the key concepts at the end of each chapter.Hematology/Hemostasis Reference Ranges inside the front and back covers provide quick reference.Three new chapters detail significant information: An Overview of Clinical Laboratory Hematology, Hematology in Selected Populations, and Monitoring Anticoagulant Therapy.All chapters have been revised to feature the most current technology and research updates.The included Glossary of Terms and consistent pedagogy ensure a cohesive, effective tool for learning.Two well-respected editors contributed more significantly to this third edition: George Fritsma shared his expertise for the Hemostasis section and Kathy Doig managed the pedagogical features.

    More recently, "computational thinking" has become part of the K-12 curriculum. But what is computational thinking? This volume in the MIT Press Essential Knowledge series offers an accessible overview, tracing a genealogy that begins centuries before digital computers and portraying computational thinking as pioneers of computing have described it.The authors explain that computational thinking (CT) is not a set of concepts for programming; it is a way of thinking that is honed through practice: the mental skills for designing computations to do jobs for us, and for explaining and interpreting the world as a complex of information processes. Mathematically trained experts (known as "computers") who performed complex calculations as teams engaged in CT long before electronic computers. The authors identify six dimensions of today's highly developed CT--methods, machines, computing education, software engineering, computational science, and design--and cover each in a chapter. Along the way, they debunk inflated claims for CT and computation while making clear the power of CT in all its complexity and multiplicity.

    It covers the whole spectrum of its beliefs, values, social principles, cultural institutions, and contemporary problems. Edited by Khurshid Ahmad, this book brings together leading Muslim scholarship and covers ideology, culture, the concept of worship, social justice, women in Islam, political theory in Islam, and the objectives of the Islamic economic order. It also discusses what Islam gave to humanity, the Western world and its challenges to Islam, and Islam and the crisis of the modern world.

    Acclaimed data scientist DJ Patil details a new approach to solving problems in Data Jujitsu.Learn how to use a problem's "weight" against itself to:Break down seemingly complex data problems into simplified partsUse alternative data analysis techniques to examine themUse human input, such as Mechanical Turk, and design tricks that enlist the help of your users to take short cuts around tough problemsLearn more about the problems before starting on the solutions—and use the findings to solve them, or determine whether the problems are worth solving at all.

    

    However, despite all of the recent advances in computer vision research, the dream of having a computer interpret an image at the same level as a two-year old remains elusive. Why is computer vision such a challenging problem and what is the current state of the art?Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos.More than just a source of "recipes," this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniquesTopics and features: Structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses Presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects Provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory Suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book Supplies supplementary course material for students at the associated website, http: //szeliski.org/Book/ Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

    In this indispensable volume, a primer for the information age, Hans Christian von Baeyer presents a clear description of what information is, how concepts of its measurement, meaning, and transmission evolved, and what its ever-expanding presence portends for the future. Information is poised to replace matter as the primary stuff of the universe, von Baeyer suggests; it will provide a new basic framework for describing and predicting reality in the twenty-first century. Despite its revolutionary premise, von Baeyer's book is written simply in a straightforward fashion, offering a wonderfully accessible introduction to classical and quantum information. Enlivened with anecdotes from the lives of philosophers, mathematicians, and scientists who have contributed significantly to the field, Information conducts readers from questions of subjectivity inherent in classical information to the blurring of distinctions between computers and what they measure or store in our quantum age. A great advance in our efforts to define and describe the nature of information, the book also marks an important step forward in our ability to exploit information--and, ultimately, to transform the nature of our relationship with the physical universe. (20040301)

    When we use consumer products from Google, Microsoft, Facebook, Apple, or Baidu, we are often interacting with a deep learning system. In this volume in the MIT Press Essential Knowledge series, computer scientist John Kelleher offers an accessible and concise but comprehensive introduction to the fundamental technology at the heart of the artificial intelligence revolution.Kelleher explains that deep learning enables data-driven decisions by identifying and extracting patterns from large datasets; its ability to learn from complex data makes deep learning ideally suited to take advantage of the rapid growth in big data and computational power. Kelleher also explains some of the basic concepts in deep learning, presents a history of advances in the field, and discusses the current state of the art. He describes the most important deep learning architectures, including autoencoders, recurrent neural networks, and long short-term networks, as well as such recent developments as Generative Adversarial Networks and capsule networks. He also provides a comprehensive (and comprehensible) introduction to the two fundamental algorithms in deep learning: gradient descent and backpropagation. Finally, Kelleher considers the future of deep learning—major trends, possible developments, and significant challenges.

    This text is split into distinct sections, each concentrating on a core angle of the language. The book also contains sections for Web and application development, the two most popular uses for Python. It is designed to teach a programmer how to use Python by explaining the mechanics of Python. The appendixes offer a quick guide to the main features of the Python language, as well as additional guides to non-essential systems such as the IDLE development environment and general guidelines for migrating from another language.

    Author Chad Waterbury offers the most current neuromuscular science to debunk the fitness myths and conventional wisdom that may be wreaking havoc on your workouts and inhibiting your gains. Forget lifting moderate weights slowly for lots and lots of sets and reps. The best way to get huge in a hurry is to use heavy weights and lift them quickly for fewer repetitions. Waterbury's groundbreaking programs will enable you to: -Add Mass and size. Gain as much as 16 pounds of muscle in 16 weeks--and add 1 full inch of upper arm circumference in half that time!-Get stronger...fast! Even seasoned lifters can realize a 5 percent increase in strength in the first few weeks. And in 12 weeks, you can boost your overall strength by up to 38 percent.-Build power and stamina. Increase your one-rep max in your core lifts by as much as 30 percent.-Shed fat fast. Burn off up to 10 pounds of body fat, losing up to 2 pounds of fat per week.With Men's Health Huge in a Hurry, you'll not only get bigger faster, you'll do it with less time wasted in the gym and with less post workout pain and a much lower injury risk.