1.

Record Nr.

UNINA990000527500403321

Autore

International conference on software engineering : <12. ;  : 1990

Titolo

12th international conference on software engineering : march 26-30, 1990, Nice, France

Pubbl/distr/stampa

Los Alamitos, California : IEEE Computer Society Press, ©1990

ISBN

0-8186-2026-9

Descrizione fisica

XVII, 337 p. : ill. ; 28 cm

Disciplina

005.3

Locazione

DINEL

Collocazione

10 PRO 402

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

2.

Record Nr.

UNINA9910821542603321

Autore

Bellanca James A

Titolo

Shifting to digital : a guide to engaging, teaching, & assessing remote learners / / James A. Bellanca, Gwendolyn Battle Lavert, Kate Bellanca

Pubbl/distr/stampa

Bloomington, Indiana : , : Solution Tree Press, , [2022]

©2022

ISBN

1-952812-22-4

Edizione

[1st ed.]

Descrizione fisica

1 online resource (x, 255 pages) : illustrations, forms, portraits

Collana

Gale eBooks

Disciplina

371.33/44678

Soggetti

Web-based instruction - Planning

Web-based instruction - Evaluation

Lesson planning

Educational tests and measurements

Internet in education

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia



Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

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.

Sommario/riassunto

"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"--

3.

Record Nr.

UNINA9910483614903321

Autore

White Lyndon

Titolo

Neural Representations of Natural Language / / by Lyndon White, Roberto Togneri, Wei Liu, Mohammed Bennamoun

Pubbl/distr/stampa

Singapore : , : Springer Singapore : , : Imprint : Springer, , 2019

ISBN

981-13-0062-3

Edizione

[1st ed. 2019.]

Descrizione fisica

1 online resource (XIV, 122 p. 36 illus., 31 illus. in color.)

Collana

Studies in Computational Intelligence, , 1860-949X ; ; 783

Disciplina

006.3

Soggetti

Computational intelligence

Signal processing

Image processing

Speech processing systems

Pattern perception

Computational linguistics

Computational Intelligence

Signal, Image and Speech Processing

Pattern Recognition

Computational Linguistics

Lingua di pubblicazione

Inglese



Formato

Materiale a stampa

Livello bibliografico

Monografia

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

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.

Sommario/riassunto

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.