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1. |
Record Nr. |
UNINA990000527500403321 |
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Autore |
International conference on software engineering : <12. ; : 1990 |
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Titolo |
12th international conference on software engineering : march 26-30, 1990, Nice, France |
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Pubbl/distr/stampa |
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Los Alamitos, California : IEEE Computer Society Press, ©1990 |
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ISBN |
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Descrizione fisica |
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XVII, 337 p. : ill. ; 28 cm |
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Locazione |
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Collocazione |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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2. |
Record Nr. |
UNINA9910821542603321 |
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Autore |
Bellanca James A |
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Titolo |
Shifting to digital : a guide to engaging, teaching, & assessing remote learners / / James A. Bellanca, Gwendolyn Battle Lavert, Kate Bellanca |
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Pubbl/distr/stampa |
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Bloomington, Indiana : , : Solution Tree Press, , [2022] |
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©2022 |
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ISBN |
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Edizione |
[1st ed.] |
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Descrizione fisica |
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1 online resource (x, 255 pages) : illustrations, forms, portraits |
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Collana |
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Disciplina |
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Soggetti |
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Web-based instruction - Planning |
Web-based instruction - Evaluation |
Lesson planning |
Educational tests and measurements |
Internet in education |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Intro -- Acknowledgments -- Table of Contents -- About the Authors -- Introduction -- Chapter 1 -- Chapter 2 -- Chapter 3 -- Chapter 4 -- Chapter 5 -- Chapter 6 -- Chapter 7 -- Chapter 8 -- Chapter 9 -- References and Resources -- Index. |
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Sommario/riassunto |
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"Virtual learning is more important than ever as schools across the world transition to digital classrooms. With their book Shifting to Digital: A Guide to Engaging, Teaching, and Assessing Remote Learners, James A. Bellanca, Gwendolyn Battle Lavert, and Kate Bellanca mine the most recent research and best practices to provide a broad guide for maximizing the potential of remote learning. They provide specific strategies for handling technology, planning high-engagement instruction, assessing collaboration and assignments, and more. Additionally, you will gain access to a helpful list of digital tools, along with online-specific lessons and projects for various subjects and grades. Shifting to Digital is a comprehensive resource for teachers to use as they attempt to transition smoothly to a new era of education"-- |
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3. |
Record Nr. |
UNINA9910483614903321 |
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Autore |
White Lyndon |
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Titolo |
Neural Representations of Natural Language / / by Lyndon White, Roberto Togneri, Wei Liu, Mohammed Bennamoun |
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Pubbl/distr/stampa |
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Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (XIV, 122 p. 36 illus., 31 illus. in color.) |
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Collana |
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Studies in Computational Intelligence, , 1860-949X ; ; 783 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Signal processing |
Image processing |
Speech processing systems |
Pattern perception |
Computational linguistics |
Computational Intelligence |
Signal, Image and Speech Processing |
Pattern Recognition |
Computational Linguistics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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Introduction -- Machine Learning for Representations -- Current Challenges in Natural Language Processing -- Word Representations -- Word Sense Representations -- Phrase Representations -- Sentence representations and beyond -- Character-Based Representations -- Conclusion. |
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Sommario/riassunto |
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This book offers an introduction to modern natural language processing using machine learning, focusing on how neural networks create a machine interpretable representation of the meaning of natural language. Language is crucially linked to ideas – as Webster’s 1923 “English Composition and Literature” puts it: “A sentence is a group of words expressing a complete thought”. Thus the representation of sentences and the words that make them up is vital in advancing artificial intelligence and other “smart” systems currently being developed. Providing an overview of the research in the area, from Bengio et al.’s seminal work on a “Neural Probabilistic Language Model” in 2003, to the latest techniques, this book enables readers to gain an understanding of how the techniques are related and what is best for their purposes. As well as a introduction to neural networks in general and recurrent neural networks in particular, this book details the methods used for representing words, senses of words, and larger structures such as sentences or documents. The book highlights practical implementations and discusses many aspects that are often overlooked or misunderstood. The book includes thorough instruction on challenging areas such as hierarchical softmax and negative sampling, to ensure the reader fully and easily understands the details of how the algorithms function. Combining practical aspects with a more traditional review of the literature, it is directly applicable to a broad readership. It is an invaluable introduction for early graduate students working in natural language processing; a trustworthy guide for industry developers wishing to make use of recent innovations; and a sturdy bridge for researchers already familiar with linguistics or machine learning wishing to understand the other. |
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