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Museums and Interactive Virtual Learning
Museums and Interactive Virtual Learning provides informal educators with practical resources that will help them to build dynamic digital engagement experiences within their own cultural organizations. Presenting vignettes from experienced museum educators and end users, as well as scientific data and practical resources, the book highlights the mutual benefits that Interactive Virtual Learning (IVL) programs offer to the museum and those visiting from a distance.Chapters mirror the step-by-step process of developing reputable IVL programs and emphasize how important it is for cultural organizations to encourage cross-departmental collaboration, if they wish to ensure that their programs align with the overall goals of the organization.Providing a thorough overview of the technologies, budget, marketing and staff requirements, the authors offer a realistic depiction of the work involved in building content for digital engagement.Emphasizing the importance of assessing existing programming, the book shows how institutions can adapt content to fit a virtual format and create inclusive digital engagement opportunities that reach local, national, and international audiences. Museums and Interactive Virtual Learning is an essential guide for professionals who are tasked with interpreting the content of a cultural organization and building lasting digital engagement opportunities.It will be particularly useful to those looking to reach diverse audiences.
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Machine Learning for Text
This second edition textbook covers a coherently organized framework for text analytics, which integrates material drawn from the intersecting topics of information retrieval, machine learning, and natural language processing.Particular importance is placed on deep learning methods.The chapters of this book span three broad categories:1.Basic algorithms: Chapters 1 through 7 discuss the classical algorithms for text analytics such as preprocessing, similarity computation, topic modeling, matrix factorization, clustering, classification, regression, and ensemble analysis. 2. Domain-sensitive learning and information retrieval: Chapters 8 and 9 discuss learning models in heterogeneous settings such as a combination of text with multimedia or Web links.The problem of information retrieval and Web search is also discussed in the context of its relationship with ranking and machine learning methods. 3. Natural language processing: Chapters 10 through 16 discuss various sequence-centric and natural language applications, such as feature engineering, neural language models, deep learning, transformers, pre-trained language models, text summarization, information extraction, knowledge graphs, question answering, opinion mining, text segmentation, and event detection.Compared to the first edition, this second edition textbook (which targets mostly advanced level students majoring in computer science and math) has substantially more material on deep learning and natural language processing.Significant focus is placed on topics like transformers, pre-trained language models, knowledge graphs, and question answering.
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FIA Management Information MA1 : Interactive Text
Foundations in Accountancy (FIA) awards are entry-level, core-skill focused qualifications from ACCA. They provide flexible options for students and employers, and as an ACCA Approved Content Provider, BPP Learning Media s suite of study tools will provide you with all the accurate and up-to-date material you need for exam success.
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Technologies, Innovation, and Change in Personal and Virtual Learning Environments
Over the last decade there continues to be an increase in the technology and how it affects our lives.Since then the incorporation of electronic databases and other communication tools for students, faculty and staff, virtual learning environments have become an important innovation in the student learning experience. Technologies, Innovation, and Change in Personal and Virtual Learning Environments presents a widespread collection of research on the growth, innovation and implementation of learning technologies for educators, technologists and trainers.The book is a useful source for academics and professionals interested in information and communication technologies.
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Science, Technology And Innovation Indicators : Lessons from the Development Experience in Africa
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Ontology Learning from Text : Methods, Evaluation and Applications
This volume brings together ontology learning, knowledge acquisition and other related topics.It presents current research in ontology learning, addressing three perspectives.The first perspective looks at methodologies that have been proposed to automatically extract information from texts and to give a structured organization to such knowledge, including approaches based on machine learning techniques.Then there are evaluation methods for ontology learning, aiming at defining procedures and metrics for a quantitative evaluation of the ontology learning task; and finally application scenarios that make ontology learning a challenging area in the context of real applications such as bio-informatics.According to the three perspectives mentioned above, the book is divided into three sections, each including a selection of papers addressing respectively the methods, the applications and the evaluation of ontology learning approaches.
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Supervised Machine Learning for Text Analysis in R
Text data is important for many domains, from healthcare to marketing to the digital humanities, but specialized approaches are necessary to create features for machine learning from language.Supervised Machine Learning for Text Analysis in R explains how to preprocess text data for modeling, train models, and evaluate model performance using tools from the tidyverse and tidymodels ecosystem.Models like these can be used to make predictions for new observations, to understand what natural language features or characteristics contribute to differences in the output, and more.If you are already familiar with the basics of predictive modeling, use the comprehensive, detailed examples in this book to extend your skills to the domain of natural language processing.This book provides practical guidance and directly applicable knowledge for data scientists and analysts who want to integrate unstructured text data into their modeling pipelines.Learn how to use text data for both regression and classification tasks, and how to apply more straightforward algorithms like regularized regression or support vector machines as well as deep learning approaches.Natural language must be dramatically transformed to be ready for computation, so we explore typical text preprocessing and feature engineering steps like tokenization and word embeddings from the ground up.These steps influence model results in ways we can measure, both in terms of model metrics and other tangible consequences such as how fair or appropriate model results are.
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FIA Recording Financial Transactions FA1 : Interactive Text
Foundations in Accountancy (FIA) awards are entry-level, core-skill focused qualifications from ACCA. They provide flexible options for students and employers, and as an ACCA Approved Content Provider, BPP Learning Media s suite of study tools will provide you with all the accurate and up-to-date material you need for exam success.
Price: 25.00 £ | Shipping*: 3.99 £
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