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Now students can master the nuances of Microsoft Office quickly with ILLUSTRATED MICROSOFT OFFICE 365 & OFFICE 2019 INTRODUCTORY, part of today's popular Illustrated Series. This focused, user-friendly approach uses a proven two-page layout that allows students to work through an entire task without turning the page. Clear Learning Outcomes outline the skills for each lesson, while large full-color screen images reflect exactly what students see on their own computers. Each module begins with a brief overview of the principles covered in the lesson and introduces a real-world case scenario to engage students and reinforce critical skills to make them successful in their educational and professional careers. In addition, MindTap and updated SAM (Skills Assessment Manager) online resources are available to guide additional study and ensure successful results.

Now students can master the nuances of Microsoft Office quickly with ILLUSTRATED MICROSOFT OFFICE 365 & WORD 2019 COMPREHENSIVE, part of today's popular Illustrated Series. This focused, user-friendly approach uses a proven two-page layout that allows students to work through an entire task without turning the page. Clear Learning Outcomes outline the skills for each lesson, while large full-color screen images reflect exactly what students see on their own computers. Each module begins with a brief overview of the principles covered in the lesson and introduces a real-world case scenario to engage students and reinforce critical skills to make them successful in their educational and professional careers. In addition, MindTap and updated SAM (Skills Assessment Manager) online resources are available to guide additional study and ensure successful results.

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

A new, thoroughly updated edition of a comprehensive overview of knowledge management (KM), covering theoretical foundations, the KM process, tools, and professions. The ability to manage knowledge has become increasingly important in today's knowledge economy. Knowledge is considered a valuable commodity, embedded in products and in the tacit knowledge of highly mobile individual employees. Knowledge management (KM) represents a deliberate and systematic approach to cultivating and sharing an organization's knowledge base. This textbook and professional reference offers a comprehensive overview of the field. Drawing on ideas, tools, and techniques from such disciplines as sociology, cognitive science, organizational behavior, and information science, it describes KM theory and practice at the individual, community, and organizational levels. Chapters cover such topics as tacit and explicit knowledge, theoretical modeling of KM, the KM cycle from knowledge capture to knowledge use, KM tools, KM assessment, and KM professionals. This third edition has been completely revised and updated to reflect advances in the dynamic and emerging field of KM. The specific changes include extended treatment of tacit knowledge; integration of such newer technologies as social media, visualization, mobile technologies, and crowdsourcing; a new chapter on knowledge continuity, with key criteria for identifying knowledge at risk; material on how to identify, document, validate, share, and implement lessons learned and best practices; the addition of new categories of KM jobs; and a new emphasis on the role of KM in innovation. Supplementary materials for instructors are available online.

This volume is based on contributions from the First International Conference on “Recent Advances in Natural Language Processing” (RANLP'95) held in Tzigov Chark, Bulgaria, 14-16 September 1995. This conference was one of the most important and competitively reviewed conferences in Natural Language Processing (NLP) for 1995 with submissions from more than 30 countries. Of the 48 papers presented at RANLP'95, the best (revised) papers have been selected for this book, in the hope that they reflect the most significant and promising trends (and latest successful results) in NLP. The book is organised thematically and the contributions are grouped according to the traditional topics found in NLP: morphology, syntax, grammars, parsing, semantics, discourse, grammars, generation, machine translation, corpus processing and multimedia. To help the reader find his/her way, the authors have prepared an extensive index which contains major terms used in NLP; an index of authors which lists the names of the authors and the page numbers of their paper(s); a list of figures; and a list of tables. This book will be of interest to researchers, lecturers and graduate students interested in Natural Language Processing and more specifically to those who work in Computational Linguistics, Corpus Linguistics and Machine Translation.

As an official publication of the National College Learning Center Association (NCLCA), The Learning Assistance Review (TLAR) seeks to foster communication among higher education learning center professionals. Its audience includes learning center administrators, teaching staff, and professional or student worker tutors, consultants, mentors, and faculty members and administrators who are interested in improving the learning skills of post-secondary students. NCLCA defines a learning center at institutions of higher education as interactive, academic spaces that exist to reinforce and extend student learning in physical and/or virtual environments. A variety of comprehensive support services and programs are offered in these environments to enhance student academic success, retention, and completion rates by apply best practices, student learning theories, and addressing student-learning needs from multiple pedagogical perspectives. Staffed by professionals, paraprofessionals, faculty, and/or trained student educators, learning centers are designed to reinforce the holistic academic growth of students by fostering critical thinking, metacognitive development, and academic personal success.TLAR aims to publish scholarly articles and reviews that address issues of interest to a broad range of academic professionals. Primary consideration will be given to articles about program design and evaluation, classroom-based research, the application of theory and research to practice, innovative teaching strategies, student assessment, and other topics that bridge the gaps within our diverse and growing profession.The journal is published twice a year. All submissions are subject to a masked, double-blind review process. Administrators use their published work to continue the growth and development of the learning commons space on campus, while faculty may use their publications toward promotion and tenure at institutions where student success initiatives are rewarded as part of the process.