Here, he contributes a new epilogue to his collection of classic lectures on the relationship of fiction to age-old concepts of apocalyptic chaos and crisis. Prompted by the approach of the millennium, he revisits the book which brings his highly concentrated insights to bear on some of the most unyielding philosophical and aesthetic enigmas. Examining the works of writers from Plato to William Burrows, Kermode shows how they have persistently imposed their "fictions" upon the face of eternity and how these have reflected the apocalyptic spirit. Kermode then discusses literature at a time when new fictive explanations, as used by Spenser and Shakespeare, were being devised to fit a world of uncertain beginning and end. He goes on to deal perceptively with modern literaturewith "traditionalists" such as Yeats, Eliot, and Joyce, as well as contemporary "schismatics," the French "new novelists," and such seminal figures as Jean-Paul Sartre and Samuel Beckett. Whether weighing the difference between modern and earlier modes of apocalyptic thought, considering the degeneration of fiction into myth, or commenting on the vogue of the Absurd, Kermode is distinctly lucid, persuasive, witty, and prodigal of ideas.

    A landmark achievement as expansive, erudite, and passionate as its renowned author, Shakespeare: The Invention of the Human is the culmination of a lifetime of reading, writing about, and teaching Shakespeare. Preeminent literary critic-and ultimate authority on the western literary tradition-Harold Bloom leads us through a comprehensive reading of every one of the dramatist's plays, brilliantly illuminating each work with unrivaled warmth, wit and insight. At the same time, Bloom presents one of the boldest theses of Shakespearean scholarships: that Shakespeare not only invented the English language, but also created human nature as we know it today.

    Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of Nathan Yau, we can. With this full-color book, data visualization guru and author Nathan Yau uses step-by-step tutorials to show you how to visualize and tell stories with data. He explains how to gather, parse, and format data and then design high quality graphics that help you explore and present patterns, outliers, and relationships.Presents a unique approach to visualizing and telling stories with data, from a data visualization expert and the creator of flowingdata.com, Nathan Yau Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbers Details tools that can be used to visualize data-native graphics for the Web, such as ActionScript, Flash libraries, PHP, and JavaScript and tools to design graphics for print, such as R and Illustrator Contains numerous examples and descriptions of patterns and outliers and explains how to show them Visualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing.

    Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.You have been predicted — by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die.Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future — lifting a bit of the fog off our hazy view of tomorrow — means pay dirt.In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: -What type of mortgage risk Chase Bank predicted before the recession. -Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves. -Why early retirement decreases life expectancy and vegetarians miss fewer flights. -Five reasons why organizations predict death, including one health insurance company. -How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual. -How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! -How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. -How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free. -What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward — but that can be predicted in advance?Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

    An analysis of Victorian women writers, this pathbreaking book of feminist literary criticism is now reissued with a substantial new introduction by Sandra Gilbert and Susan Gubar that reveals the origins of their revolutionary realization in the 1970s that "the personal was the political, the sexual was the textual."Contents:The Queen's looking glass: female creativity, male images of women, and the metaphor of literary paternity --Infection in the sentence: the women writer and the anxiety of authorship --The parables of the cave --Shut up in prose: gender and genre in Austen's Juvenilia --Jane Austen's cover story (and its secret agents) --Milton's bogey: patriarchal poetry and women readers --Horror's twin: Mary Shelley's monstrous Eve --Looking oppositely: Emily Brontë's bible of hell --A secret, inward wound: The professor's pupil --A dialogue of self and soul: plain Jane's progress --The genesis of hunger, according to Shirley --The buried life of Lucy Snowe --Made keen by loss: George Eliot's veiled vision --George Eliot as the angel of destruction --The aesthetics of renunciation --A woman, white: Emily Dickinson's yarn of pearl.

    This highly anticipated fourth edition of the most ...Download Link : readmeaway.com/download?i=0128042915            0128042915 Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF by Ian H. WittenRead Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems) PDF from Morgan Kaufmann,Ian H. WittenDownload Ian H. Witten's PDF E-book Data Mining: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems)

    The R language is recognised as one of the most powerful and flexible statistical software packages, and it enables the user to apply many statistical techniques that would be impossible without such software to help implement such large data sets.

    Now that machine learning is thriving, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks. If you have some programming experience and you’re ready to code a machine learning project, this guide is for you.This hands-on book shows you how to use:Scikit-Learn, an accessible framework that implements many algorithms efficiently and serves as a great machine learning entry pointTensorFlow, a more complex library for distributed numerical computation, ideal for training and running very large neural networksPractical code examples that you can apply without learning excessive machine learning theory or algorithm details

    Falling into Theory is a brief and inexpensive collection of essays that asks literature students to think about the fundamental questions of literary studies today.

    A brilliant display of erudition, wit, and wisdom, his exploration of how great European writers from Homer to Virginia Woolf depicted reality has taught generations how to read Western literature. This new expanded edition includes a substantial essay in introduction by Edward Said as well as an essay, never before translated into English, in which Auerbach responds to his critics.A German Jew, Auerbach was forced out of his professorship at the University of Marburg in 1935. He left for Turkey, where he taught at the state university in Istanbul. There he wrote "Mimesis," publishing it in German after the end of the war. Displaced as he was, Auerbach produced a work of great erudition that contains no footnotes, basing his arguments instead on searching, illuminating readings of key passages from his primary texts. His aim was to show how from antiquity to the twentieth century literature progressed toward ever more naturalistic and democratic forms of representation. This essentially optimistic view of European history now appears as a defensive--and impassioned--response to the inhumanity he saw in the Third Reich. Ranging over works in Greek, Latin, Spanish, French, Italian, German, and English, Auerbach used his remarkable skills in philology and comparative literature to refute any narrow form of nationalism or chauvinism, in his own day and ours. For many readers, both inside and outside the academy, "Mimesis" is among the finest works of literary criticism ever written.

    It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.”Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.

    Derek Attridge argues that such resistance represents not a dead end, but a crucial starting point from which to explore anew the power and practices of Western art.In this lively, original volume, the author:considers the implications of regarding the literary work as an innovative cultural event, both in its time and for later generations; provides a rich new vocabulary for discussions of literature, rethinking such terms as invention, singularity, otherness, alterity, performance and form; returns literature to the realm of ethics, and argues the ethical importance of the literary institution to a culture; demonstrates how a new understanding of the literary might be put to work in a 'responsible, ' creative mode of reading.The Singularity of Literature is not only a major contribution to the theory of literature, but also a celebration of the extraordinary pleasure of the literary, for reader, writer, student or critic.

    His selection of essays, each important in its own right, also serves as ‘the best... introduction so far to Barthes’ career as the slayer of contemporary myths’. (John Sturrock, New Statesman)

    He takes us into the secret world of "Survivor" Spoilers, where avid internet users pool their knowledge to unearth the show's secrets before they are revealed on the air. He introduces us to young "Harry Potter" fans who are writing their own Hogwarts tales while executives at Warner Brothers struggle for control of their franchise. He shows us how "The Matrix" has pushed transmedia storytelling to new levels, creating a fictional world where consumers track down bits of the story across multiple media channels.Jenkins argues that struggles over convergence will redefine the face of American popular culture. Industry leaders see opportunities to direct content across many channels to increase revenue and broaden markets. At the same time, consumers envision a liberated public sphere, free of network controls, in a decentralized media environment. Sometimes corporate and grassroots efforts reinforce each other, creating closer, more rewarding relations between media producers and consumers. Sometimes these two forces are at war.Jenkins provides a riveting introduction to the world where every story gets told and every brand gets sold across multiple media platforms. He explains the cultural shift that is occurring as consumers fight for control across disparate channels, changing the way we do business, elect our leaders, and educate our children.

    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.