Topics discussed include dualism, behaviorism, the identity theory, functionalism, the computationaltheory of mind, connectionism, physicalism, mental causation, and consciousness. The text is enhanced by chapter summaries, a glossary, suggestions for further reading, and self-assessment questions.

    Senator S. I. Hayakawa discusses the role of language in human life, the many functions of language, and how language—sometimes without our knowing—shapes our thinking in this engaging and highly respected book. Provocative and erudite, it examines the relationship between language and racial and religious prejudice; the nature and dangers of advertising from a linguistic point of view; and, in an additional chapter called “The Empty Eye,” the content, form, and hidden message of television, from situation comedies to news coverage to political advertising.

    This book explores the realm of human behavior in social situations and the way that we appear to others. Dr. Goffman uses the metaphor of theatrical performance as a framework. Each person in everyday social intercourse presents himself and his activity to others, attempts to guide and control the impressions they form of him, and employs certain techniques in order to sustain his performance, just as an actor presents a character to an audience. The discussions of these social techniques offered here are based upon detailed research and observation of social customs in many regions.

    Full of insights, arguments and philosophical perspectives, the book covers an amazing array of topics. Beginning in antiquity with Democritus, it progresses through logic and set theory, computability and complexity theory, quantum computing, cryptography, the information content of quantum states and the interpretation of quantum mechanics. There are also extended discussions about time travel, Newcomb's Paradox, the anthropic principle and the views of Roger Penrose. Aaronson's informal style makes this fascinating book accessible to readers with scientific backgrounds, as well as students and researchers working in physics, computer science, mathematics and philosophy.

    It took the open source world by storm since its inception in 2005, and is used by small development shops and giants like Google, Red Hat, and IBM, and of course many open source projects.A book by Git experts to turn you into a Git expert. Introduces the world of distributed version control Shows how to build a Git development workflow.

    This accessible book introduces you to landmark studies as well as the cutting-edge research that defines this fascinating field. To help you further experiment with and understand the concepts in the text, you can use COGLAB 2.0: THE ONLINE COGNITIVE PSYCHOLOGY LABORATORY. Available at www.academic.cengage.com, COGLAB contains dozens of classic experiments designed to help you learn about cognitive concepts and how the mind works.

    The meme is also one of the most important--and controversial--concepts to emerge since 'The Origin of the Species' appeared nearly 150 years ago.In 'The Meme Machine' Susan Blackmore boldly asserts: "Just as the design of our bodies can be understood only in terms of natural selection, so the design of our minds can be understood only in terms of memetic selection." Indeed, Blackmore shows that once our distant ancestors acquired the crucial ability to imitate, a second kind of natural selection began, a survival of the fittest amongst competing ideas and behaviors. Ideas and behaviors that proved most adaptive - making tools, for example, or using language--survived and flourished, replicating themselves in as many minds as possible. These memes then passed themselves on from generation to generation by helping to ensure that the genes of those who acquired them also survived and reproduced. Applying this theory to many aspects of human life, Blackmore offers brilliant explanations for why we live in cities, why we talk so much, why we can't stop thinking, why we behave altruistically, how we choose our mates, and much more.With controversial implications for our religious beliefs, our free will, our very sense of "self," 'The Meme Machine' offers a provocative theory everyone will soon be talking about.

    You'll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.To get you started--whether you're on Windows, OS X, or Linux--author Jeroen Janssens introduces the Data Science Toolbox, an easy-to-install virtual environment packed with over 80 command-line tools.Discover why the command line is an agile, scalable, and extensible technology. Even if you're already comfortable processing data with, say, Python or R, you'll greatly improve your data science workflow by also leveraging the power of the command line.Obtain data from websites, APIs, databases, and spreadsheetsPerform scrub operations on plain text, CSV, HTML/XML, and JSONExplore data, compute descriptive statistics, and create visualizationsManage your data science workflow using DrakeCreate reusable tools from one-liners and existing Python or R codeParallelize and distribute data-intensive pipelines using GNU ParallelModel data with dimensionality reduction, clustering, regression, and classification algorithms

    They give greater emphasis to the systemic perspective, in which grammaticalization is understoodin the context of an overall model of language. Their description of grammar is grounded in a comprehensive theory, but it is a theory which evolves in the process of being applied.

    Teaching Pronunciation offers current and prospective teachers of English a comprehensive treatment of pronunciation pedagogy, drawing on current theory and practice. An overview of teaching issues from the perspective of different methodologies and second language acquisition research is provided. It has a thorough grounding in the sound system of North American English, and contains insights into how this sound system intersects with listening, morphology, and spelling. It also contains diagnostic tools, assessment measures, and suggestions for syllabus design. Follow-up exercises guide teachers in developing a range of classroom activities within a communicative framework.

    It introduces the analysis of the key elements of language--sounds, words, structures and meanings, and provides a solid foundation in all of the essential topics. The third edition has been extensively revised to include new sections on important contemporary issues in language study, including language and culture, African American English, sign language, and slang. A comprehensive glossary provides useful explanations of technical terms, and each chapter contains a range of new study questions and research tasks, with suggested answers.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    Terms and phrases such as "the global village" and "the medium is the message" are now part of the lexicon, and McLuhan's theories continue to challenge our sensibilities and our assumptions about how and what we communicate.There has been a notable resurgence of interest in McLuhan's work in the last few years, fueled by the recent and continuing conjunctions between the cable companies and the regional phone companies, the appearance of magazines such as WiRed, and the development of new media models and information ecologies, many of which were spawned from MIT's Media Lab. In effect, media now begs to be redefined. In a new introduction to this edition of Understanding Media, Harper's editor Lewis Lapham reevaluates McLuhan's work in the light of the technological as well as the political and social changes that have occurred in the last part of this century.

    In this fascinating book, Ray Jackendoff emphasizes the grammatical commonalities across languages, both spoken and signed, and discusses the implications for our understanding of language acquisition and loss.

    Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks.Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data--or anything in between--this guide makes it easier to understand and implement machine learning seamlessly.Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!