The Essential Guide to Using the Web for Research
The Essential Guide to Using the Web for Research

Author: Nigel Ford

Publisher: SAGE

Published: 2011-10-14

Total Pages: 258

ISBN-13: 1446291537

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This book will be vital reading for anyone doing research, since using the web to find high quality information is a key research skill. It introduces beginners and experts alike to the most effective techniques for searching the web, assessing and organising information and using it in a range of scenarios from undergraduate essays and projects to PhD research. Nigel Ford shows how using the web poses opportunities and challenges that impact on student research at every level, and he explains the skills needed to navigate the web and use it effectively to produce high quality work. Ford connects online skills to the research process. He helps readers to understand research questions and how to answer them by constructing arguments and presenting evidence in ways that will enhance their impact and credibility. The book includes clear and helpful coverage of beginner and advanced search tools and techniques, as well as the processes of: @!critically evaluating online information @!creating and presenting evidence-based arguments @!organizing, storing and sharing information @!referencing, copyright and plagiarism. As well as providing all the basic techniques students need to find high quality information on the web, this book will help readers use this information effectively in their own research. Nigel Ford is Professor in the University of Sheffield′s Information School.

Web.Studies
Web.Studies

Author: Ross Horsley

Publisher: Bloomsbury Academic

Published: 2004-01-30

Total Pages: 320

ISBN-13: 9780340814727

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Bringing together the work of scholars, experts, and established online authors, this comprehensive book offers an analysis of contemporary web-based culture and arts plus the impact of the web on international economics, politics, and law. Featuring an introduction to the web and how it works, the book also outlines the theories and methodology of cyberculture studies and explores aspects of everyday life online: art and commerce, global communities and the politics of Internet access and activism. This second edition has content updates and new chapters on the latest developments and controversies in cyberspace.

Learning and Teaching on the World Wide Web
Learning and Teaching on the World Wide Web

Author: Christopher R. Wolfe

Publisher: Academic Press

Published: 2001-03-21

Total Pages: 307

ISBN-13: 0127618910

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This book is about using the Internet as a teaching tool. It starts with the psychology of the learner and looks at how best to fit technology to the student, rather than the other way around. The authors include leading authorities in many areas of psychology, and the book takes a broad look at learners as people. Thus, it includes a wide range of materials from how the eye "reads" moving graphs on a Web page to how people who have never met face-to-face can interact on the Internet and create "communities" of learners. The book considers many Internet technologies, but focuses on the World Wide Web and new "hybrid" technologies that integrate the Web with other communications technologies. This book is essential to researchers is psychology and education who are interested in learning. It is also used in college and graduate courses in departments of psychology and educational psychology. Teachers and trainers at any level who are using technology in their teaching (or thinking about it) find this book very useful. Key Features * Distinguished authors with considerable expertise in their fields * Broad "intra-disciplinary" perspective on learning and teaching on the Web * Focus on the Web and emerging Web-based technologies * Special attention to conducting educational research on-line * Emphasis on the Social and Psychological Context * Analyses of effective Web-based learning resources * Firmly grounded in contemporary psychological research and theory

A Course on the Web Graph
A Course on the Web Graph

Author: Anthony Bonato

Publisher: American Mathematical Soc.

Published: 2008

Total Pages: 200

ISBN-13: 0821844679

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"A Course on the Web Graph provides a comprehensive introduction to state-of-the-art research on the applications of graph theory to real-world networks such as the web graph. It is the first mathematically rigorous textbook discussing both models of the web graph and algorithms for searching the web. After introducing key tools required for the study of web graph mathematics, an overview is given of the most widely studied models for the web graph. A discussion of popular web search algorithms, e.g. PageRank, is followed by additional topics, such as applications of infinite graph theory to the web graph, spectral properties of power law graphs, domination in the web graph, and the spread of viruses in networks. The book is based on a graduate course taught at the AARMS 2006 Summer School at Dalhousie University. As such it is self-contained and includes over 100 exercises. The reader of the book will gain a working knowledge of current research in graph theory and its modern applications. In addition, the reader will learn first-hand about models of the web, and the mathematics underlying modern search engines."--Publisher's description.

Learning How to Learn
Learning How to Learn

Author: Barbara Oakley, PhD

Publisher: Penguin

Published: 2018-08-07

Total Pages: 258

ISBN-13: 052550446X

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A surprisingly simple way for students to master any subject--based on one of the world's most popular online courses and the bestselling book A Mind for Numbers A Mind for Numbers and its wildly popular online companion course "Learning How to Learn" have empowered more than two million learners of all ages from around the world to master subjects that they once struggled with. Fans often wish they'd discovered these learning strategies earlier and ask how they can help their kids master these skills as well. Now in this new book for kids and teens, the authors reveal how to make the most of time spent studying. We all have the tools to learn what might not seem to come naturally to us at first--the secret is to understand how the brain works so we can unlock its power. This book explains: Why sometimes letting your mind wander is an important part of the learning process How to avoid "rut think" in order to think outside the box Why having a poor memory can be a good thing The value of metaphors in developing understanding A simple, yet powerful, way to stop procrastinating Filled with illustrations, application questions, and exercises, this book makes learning easy and fun.

Deep Learning
Deep Learning

Author: Ian Goodfellow

Publisher: MIT Press

Published: 2016-11-10

Total Pages: 801

ISBN-13: 0262337371

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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Using Software in Qualitative Research
Using Software in Qualitative Research

Author: Ann Lewins

Publisher: SAGE

Published: 2007-05

Total Pages: 312

ISBN-13: 9780761949237

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Using Software in Qualitative Research is an essential introduction to the practice and principles of Computer Assisted Qualitative Data Analysis (CAQDAS), helping the reader choose the most appropriate package for their needs and to get the most out of the software once they are using it. This step-by-step book considers a wide range of tasks and processes, bringing them together to demystify qualitative software and encourage flexible and critical choices and uses of software in supporting analysis. The book can be read as a whole or by chapters, building on one another to provide a holistic sense of the analytic journey without advocating a particular sequential process. Accessible and comprehensive, Using Software in Qualitative Research provides a practical but analytically-grounded guide to thinking about and using software and will be an essential companion for any qualitative researcher.

Net Works
Net Works

Author: xtine burrough

Publisher: Routledge

Published: 2012-05-23

Total Pages: 262

ISBN-13: 1136944826

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Net Works offers an inside look into the process of successfully developing thoughtful, innovative digital media. In many practice-based art texts and classrooms, technology is divorced from the socio-political concerns of those using it. Although there are many resources for media theorists, practice-based students sometimes find it difficult to engage with a text that fails to relate theoretical concerns to the act of creating. Net Works strives to fill that gap. Using websites as case studies, each chapter introduces a different style of web project--from formalist play to social activism to data visualization--and then includes the artists' or entrepreneurs' reflections on the particular challenges and outcomes of developing that web project. Scholarly introductions to each section apply a theoretical frame for the projects. A companion website offers further resources for hands-on learning. Combining practical skills for web authoring with critical perspectives on the web, Net Works is ideal for courses in new media design, art, communication, critical studies, media and technology, or popular digital/internet culture.

An Introduction to Statistical Learning
An Introduction to Statistical Learning

Author: Gareth James

Publisher: Springer Nature

Published: 2023-08-01

Total Pages: 617

ISBN-13: 3031387473

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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.