McCormick carefully argues, it can be a window that shows us how we interact with the world. The way we play is the way we live. This book is about the sport of Disc Golf, but it also is about so much more than throwing a disc at a basket. For the passionate practitioner, Disc Golf becomes a meditation, and practicing not only has the potential to make us better players, but better people as we begin to focus on what we are doing on the course that is working or not working versus what we are doing at home or in the office. "Zen and the Art of Disc Golf" is about becoming the best players we can be and in turn becoming the best possible version of ourselves through cultivation of attitude, focus, determination, and mental strength. It is about mastering the mind, body, and spirit in such a way that we score better and live better. Inside this book you will learn: -What Disc Golf can teach us about life and success -The secret formula for success on and off the course. -How to create the proper attitude and focus to become better Disc Golfers and in turn live better lives. -How visualization improves our game and our lives. -Who you need to be playing with on the course. -How to hit more chains and less trees. -How to take yourself off autopilot and elevate your scores and your game. -The 3 sides of Disc Golf and how to balance them. Most importantly, after reading this book you will walk away ready to Ace holes and Ace life. Disc Golf is life. Life is good.

    Switching from thin tailed to fat tailed distributions requires more than "changing the color of the dress." Traditional asymptotics deal mainly with either n=1 or n=∞, and the real world is in between, under the "laws of the medium numbers"-which vary widely across specific distributions. Both the law of large numbers and the generalized central limit mechanisms operate in highly idiosyncratic ways outside the standard Gaussian or Levy-Stable basins of convergence. A few examples: - The sample mean is rarely in line with the population mean, with effect on "na�ve empiricism," but can be sometimes be estimated via parametric methods. - The "empirical distribution" is rarely empirical. - Parameter uncertainty has compounding effects on statistical metrics. - Dimension reduction (principal components) fails. - Inequality estimators (Gini or quantile contributions) are not additive and produce wrong results. - Many "biases" found in psychology become entirely rational under more sophisticated probability distributions. - Most of the failures of financial economics, econometrics, and behavioral economics can be attributed to using the wrong distributions. This book, the first volume of the Technical Incerto, weaves a narrative around published journal articles.

    This race-realist endeavor exposes many inconvenient truths and will certainly become a catalyst for candid conversation.Flooded with statistics, headlines, pictures and other evidence, this book is not simply an anecdotal tale of a miserable, inner-city co-existence... it’s a war report.

    Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy.Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.

    It demonstrates to students how the concepts and theories of deviance can be applied to the world around them. The authors include both theoretical analyses and ethnographic illustrations of how deviance is socially constructed, organized, and managed. The Adlers challenge the reader to see the diversity and pervasiveness of deviance in society by covering a wide variety of deviant acts represented throughout the text. Most importantly, the Adlers present deviance as a component of society and examine the construction of deviance in terms of differential social power, whereby some members of society have the power to define other whole groups as "deviant." The book takes an "interactionist" or "constructionist" perspective on deviance, looking at the processes in society that create deviance. The authors have selected studies that are ethnographic in character, focusing on the experiences of deviants, the deviant-making process, and the ways in which people who are labeled as deviant in society react to that label.

    We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services – machine learning makes it all possible.Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

    His matrix theory is one of the bases of modern quantum mechanics, while his "uncertainty principle" has altered our whole philosophy of science.In this classic, based on lectures delivered at the University of Chicago, Heisenberg presents a complete physical picture of quantum theory. He covers not only his own contributions, but also those of Bohr, Dirac, Bose, de Broglie, Fermi, Einstein, Pauli, Schrodinger, Somerfield, Rupp, ·Wilson, Germer, and others in a text written for the physical scientist who is not a specialist in quantum theory or in modern mathematics.Partial contents: introduction (theory and experiment, fundamental concepts); critique of physical concepts of the corpuscular theory (uncertainty relations and their illustration); critique of the physical concepts of the wave theory (uncertainty relations for waves, discussion of an actual measurement of the electromagnetic field); statistical interpretation of quantum theory (mathematical considerations, interference of probabilities, Bohr's complementarity); discussion of important experiments (C. T. R. Wilson, diffraction , Einstein-Rupp, emission, absorption and dispersion of radiation, interference and conservation laws, Compton effect, radiation fluctuation phenomena, relativistic formulation of the quantum theory).An 80-page appendix on the mathematical apparatus of the quantum theory is provided for the specialist.

    But in recent years, a startling new concept has evolved that places it more firmly than ever in a special position. Known as the Anthropic Cosmological Principle, this collection of ideas holds that the existence of intelligent observers determines the fundamental structure of the Universe. In its most radical version, the Anthropic Principle asserts that intelligent information-processing must come into existence in the Universe, and once it comes into existence, it will never die out. This wide-ranging and detailed book explores the many ramifications of the Anthropic Cosmological Principle, covering the whole spectrum of human inquiry from Aristotle to Z bosons. Bringing a unique combination of skills and knowledge to the subject, John D. Barrow and Frank J. Tipler--two of the world's leading cosmologists--cover the definition and nature of life, the search for extraterrestrial intelligence, and the interpretation of the quantum theory in relation to the existence of observers. The book will be of vital interest to philosophers, theologians, mathematicians, scientists, and historians, as well as to anyone concerned with the connection between the vastness of the universe of stars and galaxies and the existence of life within it on a small planet out in the suburbs of the Milky Way.

    With an emphasis on critical self-reflection, Reflect & Relate gives students the practical skills to work through life's many challenges using better interpersonal communication. The sound theory, clear explanations, lively writing, practical activities, and vibrant design all work toward a single goal: Teaching students to make better communication choices so they can build happier and healthier interpersonal relationships.

    The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement learning, evolutionary algorithms, dimensionality reduction methods, and the important area of optimization. It treads the fine line between adequate academic rigor and overwhelming students with equations and mathematical concepts. The author addresses the topics in a practical way while providing complete information and references where other expositions can be found. He includes examples based on widely available datasets and practical and theoretical problems to test understanding and application of the material. The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.Highlights a Range of Disciplines and ApplicationsDrawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. Written in an easily accessible style, this book bridges the gaps between disciplines, providing the ideal blend of theory and practical, applicable knowledge."

    Never again will you order the Poisson Distribution in a French restaurant!This updated version features all new material.

    For Chidester, Jonestown recalls the American religious commitment to redemptive sacrifice, which for Jim Jones meant saving his followers from the evils of capitalist society. "Jonestown is ancient history," writes Chidester, but it does provide us with an opportunity "to reflect upon the strangeness of familiar... promises of redemption through sacrifice."

    Yet you'd have to fly every day for the next 26,000 years to assure yourself of dying in a crash. A leisurely canoe ride is more than 100 times deadlier. Think city streets are unsafe? You're more likely to come to harm in your own home, where every year you stand a 1 in 650 chance of being injured by your bed, mattress, or pillows—and each year 800 Americans die in accidents involving soft furnishings.We live in a world governed by fear, where packets of peanuts "may contain nuts" and children must be ever on the alert to "stranger danger." And yet, life expectancy has never been higher. Crime rates have plunged. Even unintentional injuries are down. So if we're so safe, why are we so afraid?How to Live Dangerously is a hilarious, straight-talking look at the things that terrify us. It considers life's real risks, not to mention the often ridiculous methods we've contrived to keep ourselves "safe." It encourages you to ignore fearmongers and embrace a new kind of freedom, in which we all worry a little less—and live a whole lot more.

    Ranging from tiny sketches and prose poems to longer, more conventional narratives, the stories included here are:A Country Doctor - In the Gods - An Old Folio - Before the Law - Jackals and Arabs - A Visit to the Mine - The Next Village - An Imperial Message - A Paterfamilias Perplexed - Eleven Sons - A Fratricide - A Dream - Bucket Rider - Of Metaphors - The Silence of the Sirens - Gracchus the Hunter - The Knock on the Courtyard Door - Advocates - The Vulture - At Night - A Little Fable.This e-book has a total length of around 16,000 words.

    The book assumes no prior knowledge, taking the student through every stage of their research project in manageable steps. Advice on planning and conducting studies, analyzing data, and writing up practical reports is given, and examples are provided, as well as advice on how to report results in conventional (APA) style. Unlike other introductory texts, there is practical guidance on qualitative research, as well as discussion of issues of bias, interpretation, and variance. Content on qualitative methods has been expanded for the fifth edition and now includes additional material on widely used methods, such as grounded theory, thematic analysis, interpretive phenomenological analysis (IPA), and discourse analysis. The book provides clear coverage of statistical procedures, and includes everything needed at an undergraduate level from nominal level tests, to multi-factorial ANOVA designs, multiple regression, and log linear analysis. In addition, the book provides detailed and illustrated SPSS textbook. Each chapter contains a self-test glossary, key terms, and exercises, ensuring that key concepts have been understood. Students are further supported. Students are further supported by an accompanying website that provides additional exercises, revision flash cards, links to further reading, and data for use with SPSS. The website will also include updated coverage of SPSS should a new version be launched. The bestselling research methods text for over a decade, Research Methods and Statistics in Psychology remains an invaluable resource for students of psychology throughout their studies.