This causes real problems for everyday users who need to make sense of all the information available, and for software engineers who want to make their text-based applications more useful and user-friendly. Whether building a search engine for a corporate website, automatically organizing email, or extracting important nuggets of information from the news, dealing with unstructured text can be daunting.Taming Text is a hands-on, example-driven guide to working with unstructured text in the context of real-world applications. It explores how to automatically organize text, using approaches such as full-text search, proper name recognition, clustering, tagging, information extraction, and summarization. This book gives examples illustrating each of these topics, as well as the foundations upon which they are built.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.

    Tom has a simple philosophy: you can treat Oracle as a black box and just stick data into it or you can understand how it works and exploit it as a powerful computing environment. If you choose the latter, then you’ll find that there are few information management problems that you cannot solve quickly and elegantly. This fully revised second edition covers the latest developments in Oracle Database 11g. Each feature is taught in a proof-by-example manner, not only discussing what it is, but also how it works, how to implement software using it, and the common pitfalls associated with it. Don’t treat Oracle Database as a black-box. Get this book. Get under the hood. Turbo-charge your career. Fully-revised to cover Oracle Database 11g Proof-by-example approach: Let the evidence be your guide Dives deeply into Oracle Databases’s most powerful features What you’ll learn Develop an evidence-based approach to problem solving Manage transactions in highly concurrent environments Speed access to data through table and index design Manage files and memory structures for performance and reliability Scale up through partitioning and parallel processing Load and unload data to interface with external systems Think for yourself; don’t take Tom’s word for it! Who this book is for This book is aimed at Oracle Database administrators, at PL/SQL and Java developers writing code to be deployed inside the database, and at developers of external applications who use Oracle Database as a data store. It is the go to book for those wishing to create efficient and scalable applications.

    Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    Beset from all sides by a never-ending barrage of media, how can we ensure that the most accurate information emerges and is heeded? In this book, Cass R. Sunstein develops a deeply optimisticunderstanding of the human potential to pool information, and to use that knowledge to improve our lives.In an age of information overload, it is easy to fall back on our own prejudices and insulate ourselves with comforting opinions that reaffirm our core beliefs. Crowds quickly become mobs. The justification for the Iraq war, the collapse of Enron, the explosion of the space shuttle Columbia--all ofthese resulted from decisions made by leaders and groups trapped in information cocoons, shielded from information at odds with their preconceptions. How can leaders and ordinary people challenge insular decision making and gain access to the sum of human knowledge?Stunning new ways to share and aggregate information, many Internet-based, are helping companies, schools, governments, and individuals not only to acquire, but also to create, ever-growing bodies of accurate knowledge. Through a ceaseless flurry of self-correcting exchanges, wikis, coveringeverything from politics and business plans to sports and science fiction subcultures, amass--and refine--information. Open-source software enables large numbers of people to participate in technological development. Prediction markets aggregate information in a way that allows companies, rangingfrom computer manufacturers to Hollywood studios, to make better decisions about product launches and office openings. Sunstein shows how people can assimilate aggregated information without succumbing to the dangers of the herd mentality--and when and why the new aggregation techniques are soastoundingly accurate.In a world where opinion and anecdote increasingly compete on equal footing with hard evidence, the on-line effort of many minds coming together might well provide the best path to infotopia.

    Yet information graphics is rarely taught in schools or is the focus of on-the-job training. Now, for the first time, Dona M. Wong, a student of the information graphics pioneer Edward Tufte, makes this material available for all of us. In this book, you will learn:to choose the best chart that fits your data;the most effective way to communicate with decision makers when you have five minutes of their time;how to chart currency fluctuations that affect global business;how to use color effectively;how to make a graphic “colorful” even if only black and white are available.The book is organized in a series of mini-workshops backed up with illustrated examples, so not only will you learn what works and what doesn’t but also you can see the dos and don’ts for yourself. This is an invaluable reference work for students and professional in all fields.

    It draws our attention away from the bright shiny objects of the new colonialism through elucidating the social, material and political dimensions of Artificial Intelligence.”—Geoffrey C. Bowker, University of California, Irvine What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? In this book Kate Crawford reveals how this planetary network is fueling a shift toward undemocratic governance and increased racial, gender, and economic inequality. Drawing on more than a decade of research, award‑winning science, and technology, Crawford reveals how AI is a technology of extraction: from the energy and minerals needed to build and sustain its infrastructure, to the exploited workers behind “automated” services, to the data AI collects from us.    Rather than taking a narrow focus on code and algorithms, Crawford offers us a political and a material perspective on what it takes to make artificial intelligence and where it goes wrong. While technical systems present a veneer of objectivity, they are always systems of power. This is an urgent account of what is at stake as technology companies use artificial intelligence to reshape the world.

    Now, 10 years later and in the third edition, this classic still reigns supreme as the book written by an SQL master that teaches future SQL masters. These are not just tips and techniques; Joe also offers the best solutions to old and new challenges and conveys the way you need to think in order to get the most out of SQL programming efforts for both correctness and performance.In the third edition, Joe features new examples and updates to SQL-99, expanded sections of Query techniques, and a new section on schema design, with the same war-story teaching style that made the first and second editions of this book classics.

    This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

    You d be up to your neck in normal forms before you even had a chance to wade. When Michael J. Hernandez needed a database design book to teach mere mortals like himself, there were none. So he began a personal quest to learn enough to write one. And he did.Now in its Second Edition, Database Design for Mere Mortals is a miracle for today s generation of database users who don t have the background -- or the time -- to learn database design the hard way. It s also a secret pleasure for working pros who are occasionally still trying to figure out what they were taught.Drawing on 13 years of database teaching experience, Hernandez has organized database design into several key principles that are surprisingly easy to understand and remember. He illuminates those principles using examples that are generic enough to help you with virtually any application.Hernandez s goals are simple. You ll learn how to create a sound database structure as easily as possible. You ll learn how to optimize your structure for efficiency and data integrity. You ll learn how to avoid problems like missing, incorrect, mismatched, or inaccurate data. You ll learn how to relate tables together to make it possible to get whatever answers you need in the future -- even if you haven t thought of the questions yet.If -- as is often the case -- you already have a database, Hernandez explains how to analyze it -- and leverage it. You ll learn how to identify new information requirements, determine new business rules that need to be applied, and apply them.Hernandez starts with an introduction to databases, relational databases, and the idea and objectives of database design. Next, you ll walk through the key elements of the database design process: establishing table structures and relationships, assigning primary keys, setting field specifications, and setting up views. Hernandez s extensive coverage of data integrity includes a full chapter on establishing business rules and using validation tables.Hernandez surveys bad design techniques in a chapter on what not to do -- and finally, helps you identify those rare instances when it makes sense to bend or even break the conventional rules of database design.There s plenty that s new in this edition. Hernandez has gone over his text and illustrations with a fine-tooth comb to improve their already impressive clarity. You ll find updates to reflect new advances in technology, including web database applications. There are expanded and improved discussions of nulls and many-to-many relationships; multivalued fields; primary keys; and SQL data type fields. There s a new Quick Reference database design flowchart. A new glossary. New review questions at the end of every chapter.Finally, it s worth mentioning what this book isn t. It isn t a guide to any specific database platform -- so you can use it whether you re running Access, SQL Server, or Oracle, MySQL or PostgreSQL. And it isn t an SQL guide. (If that s what you need, Michael J. Hernandez has also coauthored the superb SQL Queries for Mere Mortals). But if database design is what you need to learn, this book s worth its weight in gold. Bill CamardaBill Camarda is a consultant, writer, and web/multimedia content developer. His 15 books include Special Edition Using Word 2000 and Upgrading & Fixing Networks for Dummies, Second Edition.

    Made specifically for the small screen, within the tight constraints of broadcasting standards, what these humble movies lacked in budget and star appeal, they made up for in other ways. Often they served as an introduction to genre films, particularly horror, mirroring their theatrical counterparts with a focus on sinister cults, women in prison, haunted houses and even animals in revolt. They were also a place to address serious contemporary issues - drugs, prostitution, sexual violence and justice -albeit in a cosy domestic environment. Production of telefilms continues to this day, but their significance within the history of mass media remains under-discussed. Are You in the House Alone? seeks to address this imbalance in a series of reviews and essays by fans and critics. It looks at many of the films, the networks and names behind them, and also specific genres - everything from Stephen King adaptations to superheroes to true-life dramas. So, kickback and crack open the TV guide once more for the event that is the Movie of the Week!

    This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickham’s package development philosophy. In the process, you’ll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language. Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. You’ll learn to focus on what you want your package to do, rather than think about package structure. Learn about the most useful components of an R package, including vignettes and unit tests Automate anything you can, taking advantage of the years of development experience embodied in devtools Get tips on good style, such as organizing functions into files Streamline your development process with devtools Learn the best way to submit your package to the Comprehensive R Archive Network (CRAN) Learn from a well-respected member of the R community who created 30 R packages, including ggplot2, dplyr, and tidyr

    It is designed as a reference for students in the physical sciences and engineering.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    A step-by-step visual journey through the mathematics of neural networks, and making your own using Python and Tensorflow.