We’re told that learning is all self-discipline, that we must confine ourselves to designated study areas, turn off the music, and maintain a strict ritual if we want to ace that test, memorize that presentation, or nail that piano recital.   But what if almost everything we were told about learning is wrong? And what if there was a way to achieve more with less effort?   In How We Learn, award-winning science reporter Benedict Carey sifts through decades of education research and landmark studies to uncover the truth about how our brains absorb and retain information. What he discovers is that, from the moment we are born, we are all learning quickly, efficiently, and automatically; but in our zeal to systematize the process we have ignored valuable, naturally enjoyable learning tools like forgetting, sleeping, and daydreaming. Is a dedicated desk in a quiet room really the best way to study? Can altering your routine improve your recall? Are there times when distraction is good? Is repetition necessary? Carey’s search for answers to these questions yields a wealth of strategies that make learning more a part of our everyday lives—and less of a chore.   By road testing many of the counterintuitive techniques described in this book, Carey shows how we can flex the neural muscles that make deep learning possible. Along the way he reveals why teachers should give final exams on the first day of class, why it’s wise to interleave subjects and concepts when learning any new skill, and when it’s smarter to stay up late prepping for that presentation than to rise early for one last cram session. And if this requires some suspension of disbelief, that’s because the research defies what we’ve been told, throughout our lives, about how best to learn.   The brain is not like a muscle, at least not in any straightforward sense. It is something else altogether, sensitive to mood, to timing, to circadian rhythms, as well as to location and environment. It doesn’t take orders well, to put it mildly. If the brain is a learning machine, then it is an eccentric one. In How We Learn, Benedict Carey shows us how to exploit its quirks to our advantage.  Praise for How We Learn“This book is a revelation. I feel as if I’ve owned a brain for fifty-four years and only now discovered the operating manual.”—Mary Roach, bestselling author of Stiff and Gulp“A welcome rejoinder to the faddish notion that learning is all about the hours put in.” —The New York Times Book Review   “A valuable, entertaining tool for educators, students and parents.” —Shelf Awareness   “How We Learn is more than a new approach to learning; it is a guide to making the most out of life. Who wouldn’t be interested in that?” —Scientific American   “I know of no other source that pulls together so much of what we know about the science of memory and couples it with practical, practicable advice.”—Daniel T. Willingham, professor of psychology at the University of Virginia

    To its adherents, it is an elegant statement about learning from experience. To its opponents, it is subjectivity run amok.In the first-ever account of Bayes' rule for general readers, Sharon Bertsch McGrayne explores this controversial theorem and the human obsessions surrounding it. She traces its discovery by an amateur mathematician in the 1740s through its development into roughly its modern form by French scientist Pierre Simon Laplace. She reveals why respected statisticians rendered it professionally taboo for 150 years—at the same time that practitioners relied on it to solve crises involving great uncertainty and scanty information (Alan Turing's role in breaking Germany's Enigma code during World War II), and explains how the advent of off-the-shelf computer technology in the 1980s proved to be a game-changer. Today, Bayes' rule is used everywhere from DNA de-coding to Homeland Security.Drawing on primary source material and interviews with statisticians and other scientists, The Theory That Would Not Die is the riveting account of how a seemingly simple theorem ignited one of the greatest controversies of all time.

    In particular, Isbister addresses changes in international politics and the impact on the global order of the US-led military operations in Afghanistan and Iraq. The author also focuses on major initiatives, such as the UN's Millennium Development Goals, to confront the issue of world poverty. As with all editions of this vibrant text, Isbister writes with clarity and passion, not only about failed promises, but about hope, human potential, and the belief that a just and equitable world system is attainable.

    The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo MCMC. Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.

    This urgent report is required reading, from defense expert P.W. Singer and Council on Foreign Relations fellow Emerson Brooking.

    From art museums and university hospitals to think tanks and church charities, over 1.5 million organizations of staggering diversity share the tax-exempt 501(c)(3) designation, if little else. Many social justice organizations have joined this world, often blunting political goals to satisfy government and foundation mandates. But even as funding shrinks and government surveillance rises, many activists often find it difficult to imagine movement-building outside the nonprofit model. The Revolution Will Not Be Funded gathers original essays by radical activists from around the globe who are critically rethinking the long-term consequences of this investment. Together with educators and nonprofit staff they finally name the “nonprofit industrial complex” and ask hard questions: How did politics shape the birth of the nonprofit model? How does 501(c)(3) status allow the state to co-opt political movements? Activists--or careerists? How do we fund the movement outside this complex? Urgent and visionary, The Revolution Will Not Be Funded is an unbeholden exposé of the “nonprofit industrial complex” and its quietly devastating role in managing dissent.

    Just as a single sick person can start an epidemic of the flu, so too can a small but precisely targeted push cause a fashion trend, the popularity of a new product, or a drop in the crime rate. This widely acclaimed bestseller, in which Malcolm Gladwell explores and brilliantly illuminates the tipping point phenomenon, is already changing the way people throughout the world think about selling products and disseminating ideas.Gladwell introduces us to the particular personality types who are natural pollinators of new ideas and trends, the people who create the phenomenon of word of mouth. He analyzes fashion trends, smoking, children's television, direct mail, and the early days of the American Revolution for clues about making ideas infectious, and visits a religious commune, a successful high-tech company, and one of the world's greatest salesmen to show how to start and sustain social epidemics.

    Hadoop allowed small and medium sized companies to store huge amounts of data on cheap commodity servers in racks. The introduction of Big Data has allowed businesses to make decisions based on quantifiable analysis. Hadoop is now implemented in major organizations such as Amazon, IBM, Cloudera, and Dell to name a few. This book introduces you to Hadoop and to concepts such as ‘MapReduce’, ‘Rack Awareness’, ‘Yarn’ and ‘HDFS Federation’, which will help you get acquainted with the technology.

    The book begins by introducing the R language, including the development environment. Focusing on practical solutions, the book also offers a crash course in practical statistics and covers elegant methods for dealing with messy and incomplete data using features of R.About the TechnologyR is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data.About the BookR in Action is a language tutorial focused on practical problems. It presents useful statistics examples and includes elegant methods for handling messy, incomplete, and non-normal data that are difficult to analyze using traditional methods. And statistical analysis is only part of the story. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's InsidePractical data analysis, step by stepInterfacing R with other softwareUsing R to visualize dataOver 130 graphsEight reference appendixes================================Table of ContentsPart I Getting startedIntroduction to RCreating a datasetGetting started with graphsBasic data managementAdvanced data managementPart II Basic methodsBasic graphsBasic statisticsPart III Intermediate methodsRegressionAnalysis of variancePower analysisIntermediate graphsRe-sampling statistics and bootstrappingPart IV Advanced methodsGeneralized linear modelsPrincipal components and factor analysisAdvanced methods for missing dataAdvanced graphics

    H. Evergreen, Effective Data Visualization shows readers how to create Excel charts and graphs that best communicate data findings. This comprehensive how-to guide functions as a set of blueprints--supported by research and the author's extensive experience with clients in industries all over the world--for conveying data in an impactful way. Delivered in Evergreen's humorous and approachable style, the book covers the spectrum of graph types available beyond the default options, how to determine which one most appropriately fits specific data stories, and easy steps for making the chosen graph in Excel.

    

    

    Landes's acclaimed, best-selling exploration of one of the most contentious and hotly debated questions of our time: Why do some nations achieve economic success while others remain mired in poverty? The answer, as Landes definitively illustrates, is a complex interplay of cultural mores and historical circumstance. Rich with anecdotal evidence, piercing analysis, and a truly astonishing range of erudition, The Wealth and Poverty of Nations is a "picture of enormous sweep and brilliant insight" (Kenneth Arrow) as well as one of the most audaciously ambitious works of history in decades.For the paperback edition, Landes has written a new epilogue, in which he takes account of Asian financial crisises and the international tension between overconfidence and reality.

    This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.

    Analysing various case studies of innovation-led growth, it describes the opposite situation, whereby the private sector only becomes bold enough to invest after the courageous State has made the high-risk investments.The volume argues that in the history of modern capitalism, the State has generated economic activity that would not otherwise have happened, and has actively opened up new technologies and markets that private investors can later move into. Far from the often heard criticisms of the State potentially 'crowding out' private investments, the State makes them happen, shaping and creating markets, not only 'fixing' them. Ignoring this reality only serves ideological ends, and hurts effective policymaking.This book examines case studies ranging from the advent of the Internet to the emergence of the biotechnology and nanotechnology industries. In particular, the volume debunks the myth that Silicon Valley was created by entrepreneurial venture capital. A key chapter focuses on the State investments behind Apple's success, and reveals that every major technology behind the iPhone owes its source to public funds. Thus, while entrepreneurial individuals like Steve Jobs are needed, their success is nearly impossible without their ability to ride the wave of State investments. And if Europe wants its own Googles, it needs more State action, not less.Two forward-looking chapters focus on the emergence of the next big thing after the internet: the 'green revolution'. Both solar and wind technology are currently being led by State spending, whether through the US ARPA-E programme or the Chinese and Brazilian State investment banks. The discussion refreshingly moves beyond the usual division between proponents of austerity vs. the proponents of fiscal stimulus. It argues that State investments not only help kick-start growth during periods of recession, but that they also, even in boom periods, lead to productive investments in radical new technologies which later foster decades of growth.The book ends with a fundamental question: if the State is so important to investments in high-risk innovation, why does it capture so little direct return?