Renowned classicist Lionel Casson takes us on a lively tour, from the royal libraries of the most ancient Near East, through the private and public libraries of Greece and Rome, down to the first Christian monastic libraries. To the founders of the first public libraries of the Greek world goes the credit for creating the prototype of today’s library buildings and the science of organizing books in them.Casson recounts the development of ancient library buildings, systems, holdings, and patrons, addressing questions on a wide variety of topics, such as:• What was the connection between the rise in education and literacy and the growth of libraries?• Who contributed to the early development of public libraries, especially the great library at Alexandria?• What did ancient libraries include in their holdings?• How did ancient libraries acquire books?• What was the nature of publishing in the Greek and Roman world?• How did different types of users (royalty, scholars, religious figures) and different kinds of “books” (tablets, scrolls, codices) affect library arrangements?• How did Christianity transform the nature of library holdings?Just as a library yields unexpected treasures to a meandering browser, this entertaining book offers to its perusers the surprising history of the rise and development of ancient libraries—a fascinating story never told before.

    according to an artificial intelligence trained by scientist Janelle Shane, creator of the popular blog "AI Weirdness." She creates silly AIs that learn how to name paint colors, create the best recipes, and even flirt (badly) with humans--all to understand the technology that governs so much of our daily lives.We rely on AI every day for recommendations, for translations, and to put cat ears on our selfie videos. We also trust AI with matters of life and death, on the road and in our hospitals. But how smart is AI really, and how does it solve problems, understand humans, and even drive self-driving cars?Shane delivers the answers to every AI question you've ever asked, and some you definitely haven't--like, how can a computer design the perfect sandwich? What does robot-generated Harry Potter fan-fiction look like? And is the world's best Halloween costume really "Vampire Hog Bride"?In this smart, often hilarious introduction to the most interesting science of our time, Shane shows how these programs learn, fail, and adapt--and how they reflect the best and worst of humanity. You Look Like a Thing and I Love You is the perfect book for anyone curious about what the robots in our lives are thinking.

    Ong offers fascinating insights into oral genres across the globe and through time, and examines the rise of abstract philosophical and scientific thinking. He considers the impact of orality-literacy studies not only on literary criticism and theory but on our very understanding of what it is to be a human being, conscious of self and other.This is a book no reader, writer or speaker should be without.

    The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

    Emphasizing the importance of the research process, the book shows students how social scientists design research studies, introduces the variety of observation modes used by sociologists, and covers the "how-to's" and "whys" of social research methods. Students learn how to conduct various types of research, when it is appropriate to use each method, and how to analyze qualitative and quantitative data. The 11th edition provides students with the necessary tools for understanding social research methods and for applying these concepts both inside and outside the classroom--as researchers and as consumers of research.

    Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

    With it, you'll learn how to write Python programs that work with large collections of unstructured text. You'll access richly annotated datasets using a comprehensive range of linguistic data structures, and you'll understand the main algorithms for analyzing the content and structure of written communication.Packed with examples and exercises, Natural Language Processing with Python will help you: Extract information from unstructured text, either to guess the topic or identify "named entities" Analyze linguistic structure in text, including parsing and semantic analysis Access popular linguistic databases, including WordNet and treebanks Integrate techniques drawn from fields as diverse as linguistics and artificial intelligenceThis book will help you gain practical skills in natural language processing using the Python programming language and the Natural Language Toolkit (NLTK) open source library. If you're interested in developing web applications, analyzing multilingual news sources, or documenting endangered languages -- or if you're simply curious to have a programmer's perspective on how human language works -- you'll find Natural Language Processing with Python both fascinating and immensely useful.

    The award-winning author Melanie Mitchell, a leading computer scientist, now reveals AI’s turbulent history and the recent spate of apparent successes, grand hopes, and emerging fears surrounding it.In Artificial Intelligence, Mitchell turns to the most urgent questions concerning AI today: How intelligent—really—are the best AI programs? How do they work? What can they actually do, and when do they fail? How humanlike do we expect them to become, and how soon do we need to worry about them surpassing us? Along the way, she introduces the dominant models of modern AI and machine learning, describing cutting-edge AI programs, their human inventors, and the historical lines of thought underpinning recent achievements. She meets with fellow experts such as Douglas Hofstadter, the cognitive scientist and Pulitzer Prize–winning author of the modern classic Gödel, Escher, Bach, who explains why he is “terrified” about the future of AI. She explores the profound disconnect between the hype and the actual achievements in AI, providing a clear sense of what the field has accomplished and how much further it has to go.Interweaving stories about the science of AI and the people behind it, Artificial Intelligence brims with clear-sighted, captivating, and accessible accounts of the most interesting and provocative modern work in the field, flavored with Mitchell’s humor and personal observations. This frank, lively book is an indispensable guide to understanding today’s AI, its quest for “human-level” intelligence, and its impact on the future for us all.

    Developing technology promises closeness. Sometimes it delivers, but much of our modern life leaves us less connected with people and more connected to simulations of them.In Alone Together, MIT technology and society professor Sherry Turkle explores the power of our new tools and toys to dramatically alter our social lives. It’s a nuanced exploration of what we are looking for—and sacrificing—in a world of electronic companions and social networking tools, and an argument that, despite the hand-waving of today’s self-described prophets of the future, it will be the next generation who will chart the path between isolation and connectivity.

    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.

    With this insightful book, intermediate to experienced programmers interested in data analysis will learn techniques for working with data in a business environment. You'll learn how to look at data to discover what it contains, how to capture those ideas in conceptual models, and then feed your understanding back into the organization through business plans, metrics dashboards, and other applications.Along the way, you'll experiment with concepts through hands-on workshops at the end of each chapter. Above all, you'll learn how to think about the results you want to achieve -- rather than rely on tools to think for you.Use graphics to describe data with one, two, or dozens of variablesDevelop conceptual models using back-of-the-envelope calculations, as well asscaling and probability argumentsMine data with computationally intensive methods such as simulation and clusteringMake your conclusions understandable through reports, dashboards, and other metrics programsUnderstand financial calculations, including the time-value of moneyUse dimensionality reduction techniques or predictive analytics to conquer challenging data analysis situationsBecome familiar with different open source programming environments for data analysisFinally, a concise reference for understanding how to conquer piles of data.--Austin King, Senior Web Developer, MozillaAn indispensable text for aspiring data scientists.--Michael E. Driscoll, CEO/Founder, Dataspora

    Each blockbuster has a secret history--of power, influence, dark broadcasters, and passionate cults that turn some new products into cultural phenomena. Even the most brilliant ideas wither in obscurity if they fail to connect with the right network, and the consumers that matter most aren't the early adopters, but rather their friends, followers, and imitators -- the audience of your audience.In his groundbreaking investigation, Atlantic senior editor Derek Thompson uncovers the hidden psychology of why we like what we like and reveals the economics of cultural markets that invisibly shape our lives. Shattering the sentimental myths of hit-making that dominate pop culture and business, Thompson shows quality is insufficient for success, nobody has "good taste," and some of the most popular products in history were one bad break away from utter failure. It may be a new world, but there are some enduring truths to what audiences and consumers want. People love a familiar surprise: a product that is bold, yet sneakily recognizable.Every business, every artist, every person looking to promote themselves and their work wants to know what makes some works so successful while others disappear. Hit Makers is a magical mystery tour through the last century of pop culture blockbusters and the most valuable currency of the twenty-first century--people's attention.From the dawn of impressionist art to the future of Facebook, from small Etsy designers to the origin of Star Wars, Derek Thompson leaves no pet rock unturned to tell the fascinating story of how culture happens and why things become popular.In Hit Makers, Derek Thompson investigates: - The secret link between ESPN's sticky programming and the The Weeknd's catchy choruses - Why Facebook is the world's most important modern newspaper - How advertising critics predicted Donald Trump - The 5th grader who accidentally launched "Rock Around the Clock," the biggest hit in rock and roll history - How Barack Obama and his speechwriters think of themselves as songwriters - How Disney conquered the world--but the future of hits belongs to savvy amateurs and individuals - The French collector who accidentally created the Impressionist canon - Quantitative evidence that the biggest music hits aren't always the best - Why almost all Hollywood blockbusters are sequels, reboots, and adaptations - Why one year--1991--is responsible for the way pop music sounds today - Why another year --1932--created the business model of film - How data scientists proved that "going viral" is a myth - How 19th century immigration patterns explain the most heard song in the Western Hemisphere

    Join 39 contributors as they explain how they developed simple and elegant solutions on projects ranging from the Mars lander to a Radiohead video.With Beautiful Data, you will: Explore the opportunities and challenges involved in working with the vast number of datasets made available by the Web Learn how to visualize trends in urban crime, using maps and data mashups Discover the challenges of designing a data processing system that works within the constraints of space travel Learn how crowdsourcing and transparency have combined to advance the state of drug research Understand how new data can automatically trigger alerts when it matches or overlaps pre-existing data Learn about the massive infrastructure required to create, capture, and process DNA data That's only small sample of what you'll find in Beautiful Data. For anyone who handles data, this is a truly fascinating book. Contributors include:Nathan Yau Jonathan Follett and Matt Holm J.M. Hughes Raghu Ramakrishnan, Brian Cooper, and Utkarsh Srivastava Jeff Hammerbacher Jason Dykes and Jo Wood Jeff Jonas and Lisa Sokol Jud Valeski Alon Halevy and Jayant Madhavan Aaron Koblin with Valdean Klump Michal Migurski Jeff Heer Coco Krumme Peter Norvig Matt Wood and Ben Blackburne Jean-Claude Bradley, Rajarshi Guha, Andrew Lang, Pierre Lindenbaum, Cameron Neylon, Antony Williams, and Egon Willighagen Lukas Biewald and Brendan O'Connor Hadley Wickham, Deborah Swayne, and David Poole Andrew Gelman, Jonathan P. Kastellec, and Yair Ghitza Toby Segaran

    Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.