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.

"This book investigates how those involved in education can respond to the opportunities offered by the Web 2.0 technology"--Provided by publisher.

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 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.

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.

This second edition is a practical, easy-to-read resource on web-based learning. The book ably and clearly equips readers with strategies for designing effective online courses, creating communities of web-based learners, and implementing and evaluating based on an instructional design framework. Case example, case studies, and discussion questions extend readers skills, inspire discussion, and encourage readers to explore the trends and issues related to online instructional design and delivery.

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.