An Introduction to Book History


Book Description

This is a comprehensive introduction to books and print culture which examines the move from the spoken word to written texts, the book as commodity, the power and profile of readers, and the future of the book in an electronic age.




The Scribe Method


Book Description

Ready to write your book? So why haven’t you done it yet? If you’re like most nonfiction authors, fears are holding you back. Sound familiar? Is my idea good enough? How do I structure a book? What exactly are the steps to write it? How do I stay motivated? What if I actually finish it, and it’s bad? Worst of all: what if I publish it, and no one cares? How do I know if I’m even doing the right things? The truth is, writing a book can be scary and overwhelming—but it doesn’t have to be. There’s a way to know you’re on the right path and taking the right steps. How? By using a method that’s been validated with thousands of other Authors just like you. In fact, it’s the same exact process used to produce dozens of big bestsellers–including David Goggins’s Can’t Hurt Me, Tiffany Haddish’s The Last Black Unicorn, and Joey Coleman’s Never Lose a Customer Again. The Scribe Method is the tested and proven process that will help you navigate the entire book-writing process from start to finish–the right way. Written by 4x New York Times Bestselling Author Tucker Max and publishing expert Zach Obront, you’ll learn the step-by-step method that has helped over 1,500 authors write and publish their books. Now a Wall Street Journal Bestseller itself, The Scribe Method is specifically designed for business leaders, personal development gurus, entrepreneurs, and any expert in their field who has accumulated years of hard-won knowledge and wants to put it out into the world. Forget the rest of the books written by pretenders. This is the ultimate resource for anyone who wants to professionally write a great nonfiction book.




An Introduction to Identification


Book Description

Suitable for advanced undergraduates and graduate students, this text covers the theoretical basis for mathematical modeling as well as a variety of identification algorithms and their applications. 1986 edition.




An Introduction to Statistical Learning


Book Description

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.




An Introduction to Mathematical Modeling


Book Description

Employing a practical, "learn by doing" approach, this first-rate text fosters the development of the skills beyond the pure mathematics needed to set up and manipulate mathematical models. The author draws on a diversity of fields — including science, engineering, and operations research — to provide over 100 reality-based examples. Students learn from the examples by applying mathematical methods to formulate, analyze, and criticize models. Extensive documentation, consisting of over 150 references, supplements the models, encouraging further research on models of particular interest. The lively and accessible text requires only minimal scientific background. Designed for senior college or beginning graduate-level students, it assumes only elementary calculus and basic probability theory for the first part, and ordinary differential equations and continuous probability for the second section. All problems require students to study and create models, encouraging their active participation rather than a mechanical approach. Beyond the classroom, this volume will prove interesting and rewarding to anyone concerned with the development of mathematical models or the application of modeling to problem solving in a wide array of applications.




An Introduction to Machine Learning


Book Description

This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural networks, and support vector machines. Later chapters show how to combine these simple tools by way of “boosting,” how to exploit them in more complicated domains, and how to deal with diverse advanced practical issues. One chapter is dedicated to the popular genetic algorithms. This revised edition contains three entirely new chapters on critical topics regarding the pragmatic application of machine learning in industry. The chapters examine multi-label domains, unsupervised learning and its use in deep learning, and logical approaches to induction. Numerous chapters have been expanded, and the presentation of the material has been enhanced. The book contains many new exercises, numerous solved examples, thought-provoking experiments, and computer assignments for independent work.




An Introduction to the Philosophy of Art


Book Description

Richard Eldridge presents a clear and compact survey of philosophical theories of the nature and significance of art. Drawing on materials from classical and contemporary philosophy as well as from literary theory and art criticism, he explores the representational, expressive, and formal dimensions of art, and he argues that works of art present their subject matter in ways that are of enduring cognitive, moral, and social interest. His accessible study will be invaluable to students and to all readers who are interested in the relation between thought and art.




An Introduction to Rights


Book Description

A thoroughly updated second edition that is an accessible introduction to the history, logic, moral implications and political tendencies of the idea of rights.




Introduction to Journalism


Book Description

An Introduction to Journalism examines the skills needed to work as a journalist in newspapers, television, radio, and online. This book provides case studies as a guide to researching stories, interviewing, and writing for each medium, as well as recording material for both radio and television. It offers a wide range of comments and tips on the best way to approach stories and includes interviews with journalists working on a variety of news outlets, from the BBC to weekly newspapers.




Film


Book Description

This clear, well illustrated text takes the reader through the basics of film analysis, drawing on a wide range of film for discussion. Questions of genre and the contexts and meanings of film are considered.