This book contains my answer to that question. The purpose of the book is to tell the beginning student of advanced mathematics the basic set- theoretic facts of life, and to do so with the minimum of philosophical discourse and logical formalism. The point of view throughout is that of a prospective mathematician anxious to study groups, or integrals, or manifolds. From this point of view the concepts and methods of this book are merely some of the standard mathematical tools; the expert specialist will find nothing new here. Scholarly bibliographical credits and references are out of place in a purely expository book such as this one. The student who gets interested in set theory for its own sake should know, however, that there is much more to the subject than there is in this book. One of the most beautiful sources of set-theoretic wisdom is still Hausdorff's Set theory. A recent and highly readable addition to the literature, with an extensive and up-to-date bibliography, is Axiomatic set theory by Suppes.

    The newly revised Fifth Edition has been significantly updated throughout and features substantially revised art and Running Problems in the book and on the reader Companion Website. Recognized as an extraordinary educator and active learning enthusiast, Dr. Silverthorn incorporates time-tested classroom techniques throughout the book and presents thorough, up-to-date coverage of new scientific discoveries, biotechnology techniques, and treatments of disorders. Dr. Silverthorn also co-authored the accompanying Student Workbook and Instructor Manual, ensuring that these ancillaries reinforce the pedagogical approach of the book.The Fifth Edition includes access to Interactive Physiology(R) 10-System Suite (IP-10), PhysioEx(TM) 8.0, A&P Flix animations in 3D, and The Physiology Place Companion Website. Key Topics: Introduction to Physiology, Molecular Interactions, Compartmentation: Cells and Tissues, Energy and Cellular Metabolism, Membrane Dynamics, Communication, Integration, Homeostasis, Introduction to the Endocrine System, Neurons: Cellular and Network Properties, The Central Nervous System, Sensory Physiology, Efferent Division: Autonomic and Somatic Motor Control, Muscles, Integrative Physiology I: Control of Body Movement, Cardiovascular Physiology, Blood Flow and the Control of Blood Pressure, Blood, Mechanics of Breathing, Gas Exchange and Transport, The Kidneys, Integrative Physiology II: Fluid and Electrolyte Balance, Digestion, Energy Balance and Metabolism, Endocrine Control of Growth and Metabolism, The Immune System, Integrative Physiology III: Exercise, Reproduction and DevelopmentMarket: Intended for those interested in learning the basics of human physiology

    From Edward Lorenz’s discovery of the Butterfly Effect, to Mitchell Feigenbaum’s calculation of a universal constant, to Benoit Mandelbrot’s concept of fractals, which created a new geometry of nature, Gleick’s engaging narrative focuses on the key figures whose genius converged to chart an innovative direction for science. In Chaos, Gleick makes the story of chaos theory not only fascinating but also accessible to beginners, and opens our eyes to a surprising new view of the universe.

    “Big data” refers to our burgeoning ability to crunch vast collections of information, analyze it instantly, and draw sometimes profoundly surprising conclusions from it. This emerging science can translate myriad phenomena—from the price of airline tickets to the text of millions of books—into searchable form, and uses our increasing computing power to unearth epiphanies that we never could have seen before. A revolution on par with the Internet or perhaps even the printing press, big data will change the way we think about business, health, politics, education, and innovation in the years to come. It also poses fresh threats, from the inevitable end of privacy as we know it to the prospect of being penalized for things we haven’t even done yet, based on big data’s ability to predict our future behavior.In this brilliantly clear, often surprising work, two leading experts explain what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. Big Data is the first big book about the next big thing.www.big-data-book.com

    Author Lewis Vaughn offers comprehensive treatments of core topics, including an introduction to claims and arguments, discussions of propositional and categorical logic, and full coverage of the basics of inductive reasoning. Building on this solid foundation, he also delves into areas neglected by other texts, adding extensive material on "inference to the best explanation" and on scientific reasoning; a thorough look at the evaluation of evidence and credibility; and a chapter on the psychological and social factors that can impede critical thinking. Additional notable elements are a chapter on moral reasoning, advice on how to evaluate Internet sources, and guidelines for evaluating occult, paranormal, or supernatural claims. The Power of Critical Thinking, Second Edition, integrates many pedagogical features including hundreds of diverse exercises, examples, and illustrations; progressive, stand-alone writing modules; numerous text boxes; step-by-step guidelines for evaluating claims, arguments, and explanations; a glossary of important terms; and many reminders, summaries, and review notes throughout. The text is supplemented by a companion website at www.oup.com/us/criticalthinking (offering a student study guide and more), and an Instructor's Manual with Test Questions (available both in print and on a CD). This unique text features a modular structure that allows instructors to teach the chapters in almost any order. Written in a student-friendly style and enhanced by humor where appropriate, it is ideal for courses in critical thinking, introduction to logic, informal logic, argumentative writing, and introduction to argumentation. New to the Second Edition * Full-color throughout and an expanded art program (37 more photos and illustrations) * A new writing module--an annotated sample student paper--and five additional essays for analysis * A new section on evaluating news reports and advertising * Timely discussions of intelligent design and population (nonintervention) studies * Expanded coverage of experts and authors and reasons to doubt their reliability * More "Field Problems" and exercise questions * Chapter objectives and key terms with definitions for each chapter

    The new edition provides invitations - not requirements - to use technology, as well as new conceptual problems, and new projects that focus on writing and working in teams.

    This long-awaited revision contains changes throughout the text. There are new implementations of most of the major programming systems in the book, including the interpreters and compilers, and the authors have incorporated many small changes that reflect their experience teaching the course at MIT since the first edition was published. A new theme has been introduced that emphasizes the central role played by different approaches to dealing with time in computational models: objects with state, concurrent programming, functional programming and lazy evaluation, and nondeterministic programming. There are new example sections on higher-order procedures in graphics and on applications of stream processing in numerical programming, and many new exercises. In addition, all the programs have been reworked to run in any Scheme implementation that adheres to the IEEE standard.

    Offers students with the tools necessary to do ethical research in psychology and to understand the research they learn about in psychology courses and in the media.

    The computer programmer, the doctor, the artist, and even the parent and citizen all engage in a craftsman’s work. In this thought-provoking book, Sennett explores the work of craftsmen past and present, identifies deep connections between material consciousness and ethical values, and challenges received ideas about what constitutes good work in today’s world. The Craftsman engages the many dimensions of skill—from the technical demands to the obsessive energy required to do good work. Craftsmanship leads Sennett across time and space, from ancient Roman brickmakers to Renaissance goldsmiths to the printing presses of Enlightenment Paris and the factories of industrial London; in the modern world he explores what experiences of good work are shared by computer programmers, nurses and doctors, musicians, glassblowers, and cooks. Unique in the scope of his thinking, Sennett expands previous notions of crafts and craftsmen and apprises us of the surprising extent to which we can learn about ourselves through the labor of making physical things.

    Often, computer science and computer engineering curricula don't provide students with a concentrated and consistent introduction to the fundamental concepts that underlie all computer systems. Traditional computer organization and logic design courses cover some of this material, but they focus largely on hardware design. They provide students with little or no understanding of how important software components operate, how application programs use systems, or how system attributes affect the performance and correctness of application programs. - A more complete view of systems - Takes a broader view of systems than traditional computer organization books, covering aspects of computer design, operating systems, compilers, and networking, provides students with the understanding of how programs run on real systems. - Systems presented from a programmers perspective - Material is presented in such a way that it has clear benefit to application programmers, students learn how to use this knowledge to improve program performance and reliability. They also become more effective in program debugging, because t

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    In this unconventional introduction, physicist Leonard Susskind and hacker-scientist George Hrabovsky offer a first course in physics and associated math for the ardent amateur. Unlike most popular physics books—which give readers a taste of what physicists know but shy away from equations or math—Susskind and Hrabovsky actually teach the skills you need to do physics, beginning with classical mechanics, yourself. Based on Susskind's enormously popular Stanford University-based (and YouTube-featured) continuing-education course, the authors cover the minimum—the theoretical minimum of the title—that readers need to master to study more advanced topics.An alternative to the conventional go-to-college method, The Theoretical Minimum provides a tool kit for amateur scientists to learn physics at their own pace.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    The authors present updated coverage of compilers based on research and techniques that have been developed in the field over the past few years. The book provides a thorough introduction to compiler design and covers topics such as context-free grammars, fine state machines, and syntax-directed translation.