It’s time we stand face-to-digital-face with the true powers and limitations of the algorithms that already automate important decisions in healthcare, transportation, crime, and commerce. Hello World is indispensable preparation for the moral quandaries of a world run by code, and with the unfailingly entertaining Hannah Fry as our guide, we’ll be discussing these issues long after the last page is turned.

    Updated to reflect current research and rewritten to further enhance the clarity of presentation, the fifth edition of this best-seller continues to take a linguistic-analysis approach as well and focus on the facts of language rather than theoretical approaches.

    What makes this work so important is that it returned the body to the forefront of philosophy for the first time since Plato.

    Suppose, instead of thinking about organisms using genes to reproduce themselves, as we had since Mendel's work was rediscovered, we turn it around and imagine that "our" genes build and maintain us in order to make more genes. That simple reversal seems to answer many puzzlers which had stumped scientists for years, and we haven't thought of evolution in the same way since. Drawing fascinating examples from every field of biology, he paved the way for a serious re-evaluation of evolution. He also introduced the concept of self-reproducing ideas, or memes, which (seemingly) use humans exclusively for their propagation. If we are puppets, he says, at least we can try to understand our strings.

    Covering thinkers from Aristotle to Saussure and Chomsky, "Introducing Linguistics" reveals the rules and beauty that underlie language, our most human skill.

    It provides clear, simple explanations and concrete examples of complex concepts, making a wide variety of commonly used critical theories accessible to novices without sacrificing any theoretical rigor or thoroughness.This new edition provides in-depth coverage of the most common approaches to literary analysis today: feminism, psychoanalysis, Marxism, reader-response theory, new criticism, structuralism and semiotics, deconstruction, new historicism, cultural criticism, lesbian/gay/queer theory, African American criticism, and postcolonial criticism. The chapters provide an extended explanation of each theory, using examples from everyday life, popular culture, and literary texts; a list of specific questions critics who use that theory ask about literary texts; an interpretation of F. Scott Fitzgerald's The Great Gatsby through the lens of each theory; a list of questions for further practice to guide readers in applying each theory to different literary works; and a bibliography of primary and secondary works for further reading.

    The author provides a succinct but lucid outline of the ways in which language has been defined, described, and explored, and guides readers towards further exploration of their own.

    Dreams, according to his theory, represent the hidden fulfillment of our unconscious wishes.

    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

    Wright Mills is best remembered for his highly acclaimed work The Sociological Imagination, in which he set forth his views on how social science should be pursued. Hailed upon publication as a cogent and hard-hitting critique, The Sociological Imagination took issue with the ascendant schools of sociology in the United States, calling for a humanist sociology connecting the social, personal, and historical dimensions of our lives. The sociological imagination Mills calls for is a sociological vision, a way of looking at the world that can see links between the apparently private problems of the individual and important social issues.

    From Mary, Queen of Scots, trapped by her own code, to the Navajo Code Talkers who helped the Allies win World War II, to the incredible (and incredibly simple) logisitical breakthrough that made Internet commerce secure, The Code Book tells the story of the most powerful intellectual weapon ever known: secrecy.Throughout the text are clear technical and mathematical explanations, and portraits of the remarkable personalities who wrote and broke the world’s most difficult codes. Accessible, compelling, and remarkably far-reaching, this book will forever alter your view of history and what drives it. It will also make you wonder how private that e-mail you just sent really is.

    The True Believer -- the first and most famous of his books -- was made into a bestseller when President Eisenhower cited it during one of the earliest television press conferences. Completely relevant and essential for understanding the world today, The True Believer is a visionary, highly provocative look into the mind of the fanatic and a penetrating study of how an individual becomes one.

    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.

    The techniques he presents in "How to Learn Any Language" will have you speaking, reading, writing and enjoying any foreign language you want to learn - or have to learn - in a surprisingly short time.Without beating your head against verb conjugations or noun declensions, you can follow Farber's principles and glide toward proficiency in your chosen language. His method consist of four ground-breaking but simple concepts hailed by language-teaching professionals:

    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.