With this hands-on guide, you'll learn how to write effective, idiomatic Python code by leveraging its best and possibly most neglected features. Author Luciano Ramalho takes you through Python's core language features and libraries, and shows you how to make your code shorter, faster, and more readable at the same time.Many experienced programmers try to bend Python to fit patterns they learned from other languages, and never discover Python features outside of their experience. With this book, those Python programmers will thoroughly learn how to become proficient in Python 3.This book covers:Python data model: understand how special methods are the key to the consistent behavior of objectsData structures: take full advantage of built-in types, and understand the text vs bytes duality in the Unicode ageFunctions as objects: view Python functions as first-class objects, and understand how this affects popular design patternsObject-oriented idioms: build classes by learning about references, mutability, interfaces, operator overloading, and multiple inheritanceControl flow: leverage context managers, generators, coroutines, and concurrency with the concurrent.futures and asyncio packagesMetaprogramming: understand how properties, attribute descriptors, class decorators, and metaclasses work"

    He then jets forward to the nineteenth century, where systems we can easily recognize as modern bureaucracies come into being. In some areas of life—like with the modern postal systems of Germany and France—these bureaucracies have brought tremendous efficiencies to modern life. But Graeber argues that there is a much darker side to modern bureaucracy that is rarely ever discussed. Indeed, in our own “utopia of rules,” freedom and technological innovation are often the casualties of systems that we only faintly understand.Provocative and timely, the book is a powerful look and history of bureaucracy over the ages and its power in shaping the world of ideas.

    With Mind, Paul Thagard offers an introduction to this interdisciplinary field for readers who come to the subject with very different backgrounds. It is suitable for classroom use by students with interests ranging from computer science and engineering to psychology and philosophy.Thagard's systematic descriptions and evaluations of the main theories of mental representation advanced by cognitive scientists allow students to see that there are many complementary approaches to the investigation of mind. The fundamental theoretical perspectives he describes include logic, rules, concepts, analogies, images, and connections (artificial neural networks). The discussion of these theories provides an integrated view of the different achievements of the various fields of cognitive science.This second edition includes substantial revision and new material. Part I, which presents the different theoretical approaches, has been updated in light of recent work the field. Part II, which treats extensions to cognitive science, has been thoroughly revised, with new chapters added on brains, emotions, and consciousness. Other additions include a list of relevant Web sites at the end of each chapter and a glossary at the end of the book. As in the first edition, each chapter concludes with a summary and suggestions for further reading.

    But facing the sea change that AI will bring can be paralyzing. How should companies set strategies, governments design policies, and people plan their lives for a world so different from what we know? In the face of such uncertainty, many analysts either cower in fear or predict an impossibly sunny future.But in Prediction Machines, three eminent economists recast the rise of AI as a drop in the cost of prediction. With this single, masterful stroke, they lift the curtain on the AI-is-magic hype and show how basic tools from economics provide clarity about the AI revolution and a basis for action by CEOs, managers, policy makers, investors, and entrepreneurs.When AI is framed as cheap prediction, its extraordinary potential becomes clear: Prediction is at the heart of making decisions under uncertainty. Our businesses and personal lives are riddled with such decisions. Prediction tools increase productivity--operating machines, handling documents, communicating with customers. Uncertainty constrains strategy. Better prediction creates opportunities for new business structures and strategies to compete. Penetrating, fun, and always insightful and practical, Prediction Machines follows its inescapable logic to explain how to navigate the changes on the horizon. The impact of AI will be profound, but the economic framework for understanding it is surprisingly simple.

    It features a novel way of teaching interactive designers how to create and improve their designs to incorporate the highest degree of fun. As the book shows, designing for fun is all about making interactive products like games highly entertaining, engaging, and addictive. The book's unique approach of providing a highly visual storyboard approach combined with a narrative on the art and practice of designing for fun is sure to be a hit with game and interactive designers.At first glance A Theory of Fun for Game Design is a book that will truly inspire and challenge game designers to think in new ways; however, its universal message will influence designers from all walks of life. This book captures the real essence of what drives us to seek out products and experiences that are truly fun and entertaining. The author masterfully presents his engaging theory by showing readers how many designs are lacking because they are predictable and not engaging enough. He then explains how great designers use different types of elements in new ways to make designs more fun and compelling. Anyone who is interested in design will enjoy how the book works on two levels--as a quick inspiration guide to game design, or as an informative discussion that details the insightful thinking from a great mind in the game industry.

    We have Mindstorms to thank for that. In this book, pioneering computer scientist Seymour Papert uses the invention of LOGO, the first child-friendly programming language, to make the case for the value of teaching children with computers. Papert argues that children are more than capable of mastering computers, and that teaching computational processes like de-bugging in the classroom can change the way we learn everything else. He also shows that schools saturated with technology can actually improve socialization and interaction among students and between students and teachers.

    S. Ramachandran is at the forefront of his field-so much so that Richard Dawkins dubbed him the "Marco Polo of neuroscience." Now, in a major new work, Ramachandran sets his sights on the mystery of human uniqueness. Taking us to the frontiers of neurology, he reveals what baffling and extreme case studies can teach us about normal brain function and how it evolved. Synesthesia becomes a window into the brain mechanisms that make some of us more creative than others. And autism--for which Ramachandran opens a new direction for treatment--gives us a glimpse of the aspect of being human that we understand least: self-awareness. Ramachandran tackles the most exciting and controversial topics in neurology with a storyteller's eye for compelling case studies and a researcher's flair for new approaches to age-old questions. Tracing the strange links between neurology and behavior, this book unveils a wealth of clues into the deepest mysteries of the human brain.

    Underlying this trend is a single idea: the belief that our existence is understandable through computation, and more data is enough to help us build a better world.   In actual fact, we are lost in a sea of information, increasingly divided by fundamentalism, simplistic narratives, conspiracy theories, and post-factual politics. Meanwhile, those in power use our lack of understanding to further their own interests. Despite the accessibility of information, we’re living in a new Dark Age.   From rogue financial systems to shopping algorithms, from artificial intelligence to state secrecy, we no longer understand how our world is governed or presented to us. The media is filled with unverifiable speculation, much of it generated by anonymous software, while companies dominate their employees through surveillance and the threat of automation.   In his brilliant new work, leading artist and writer James Bridle excavates the limits of technology and how it aids our understanding of the world. Surveying the history of art, technology, and information systems, he explores the dark clouds that gather over our dreams of the digital sublime.

    The latter has long been the subject of speculation, but over the past two decades researchers have developed remarkable new tools for probing the hidden, or subliminal, workings of the mind. The result of this explosion of research is a new science of the unconscious and a sea change in our understanding of how the subliminal mind affects the way we live.Employing his trademark wit and lucid, accessible explanations of the most obscure scientific subjects, Leonard Mlodinow takes us on a tour of this research, unraveling the complexities of the subliminal self and increasing our understanding of how the human mind works and how we interact with friends, strangers, spouses, and coworkers. In the process he changes our view of ourselves and the world around us.

    An important feature of the book is the large number of articles on topics of mental life, in which well-known writers discuss subjects in which they have a particular expertise. Noam Chomsky writes on his own theory of language, Idries Shah on Sufism, John Bowlby on attachment theory, B.F. Skinner on behaviorism, Oliver Sacks on nothingness, A.J. Ayer on philosophical views of the relation between mind and body, and R.D. Laing on interpersonal experience. The editor, Richard Gregory, contributes entries on aesthetics, phrenology, physiognomy, and illusions of perception. The Companion includes entries on such everyday events as sleep, humor, forgetting, and hearing, as well as specialized topics such as bilingualism, jet-lag, military incompetence, computer chess, and animal magnetism. What can, and all too often does, go wrong with the mind is also covered--many forms of mental illness are explored, as well as mental handicap, brain damage, and neurological disorders. Perception and the ways in which our senses are often deceived are treated in full, as are elements of personal development and learning, and the puzzling world of parapsychology with its altered states of consciousness, out-of-body experiences, and extra-sensory perception. The workings of the nervous system are explained in a special tutorial article. The text is supplemented by brief definitions of specialist terms and by biographies of major figures who have contributed to our understanding of the mind--individuals as varied as Plato, Johannes Kepler, William James, Sigmund Freud, and Alan Turing. The entries are arranged alphabetically and, following the style of other recent Companions, are linked by a network of helpful cross-references. The 160 illustrations have been carefully chosen to amplify the text, while specialist bibliographies provide suggestions for further reading.

    If you use Python, even as a beginner, this book will teach you practical ways to build your own machine learning solutions. With all the data available today, machine learning applications are limited only by your imagination.You'll learn the steps necessary to create a successful machine-learning application with Python and the scikit-learn library. Authors Andreas Muller and Sarah Guido focus on the practical aspects of using machine learning algorithms, rather than the math behind them. Familiarity with the NumPy and matplotlib libraries will help you get even more from this book.With this book, you'll learn:Fundamental concepts and applications of machine learningAdvantages and shortcomings of widely used machine learning algorithmsHow to represent data processed by machine learning, including which data aspects to focus onAdvanced methods for model evaluation and parameter tuningThe concept of pipelines for chaining models and encapsulating your workflowMethods for working with text data, including text-specific processing techniquesSuggestions for improving your machine learning and data science skills

    Who are these people, what motivates them, and why should you care?Consider these facts: Everything around us is turning into computers. Your typewriter is gone, replaced by a computer. Your phone has turned into a computer. So has your camera. Soon your TV will. Your car was not only designed on computers, but has more processing power in it than a room-sized mainframe did in 1970. Letters, encyclopedias, newspapers, and even your local store are being replaced by the Internet.Hackers & Painters: Big Ideas from the Computer Age, by Paul Graham, explains this world and the motivations of the people who occupy it. In clear, thoughtful prose that draws on illuminating historical examples, Graham takes readers on an unflinching exploration into what he calls “an intellectual Wild West.”The ideas discussed in this book will have a powerful and lasting impact on how we think, how we work, how we develop technology, and how we live. Topics include the importance of beauty in software design, how to make wealth, heresy and free speech, the programming language renaissance, the open-source movement, digital design, internet startups, and more.

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions.But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope.Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet. Why a spreadsheet? It's comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But don't let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data.Each chapter will cover a different technique in a spreadsheet so you can follow along: - Mathematical optimization, including non-linear programming and genetic algorithms- Clustering via k-means, spherical k-means, and graph modularity- Data mining in graphs, such as outlier detection- Supervised AI through logistic regression, ensemble models, and bag-of-words models- Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation- Moving from spreadsheets into the R programming languageYou get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. You'll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.

    “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