| 000 -LEADER |
|---|
| fixed length control field |
03367nam a22003497a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20260702142954.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
260702s20242024si a|||er|||| 001 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781009012652 [paperback] |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
University of Cebu-Banilad |
| Transcribing agency |
University of Cebu-Banilad |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Surdeanu, Mihai, |
| Relator term |
author. |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Deep learning for natural language processing : |
| Remainder of title |
a gentle introduction / |
| Statement of responsibility, etc |
Mihai Surdeanu and Marco Antonio Valezuela-Escárcega. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Singapore : |
| Name of publisher, distributor, etc |
Cambridge University Press, |
| Date of publication, distribution, etc |
c2024. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xviii, 325 pages : |
| Other physical details |
illustrations (black and white) ; |
| Dimensions |
23 cm. |
| 336 ## - CONTENT TYPE |
|---|
| Source |
rdacontent |
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Source |
rdamedia |
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Source |
rdacarrier |
| Carrier type |
volume |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Contents: List of figures — List of Tables — Preface — 1 Introduction — 2 The perception — 3 Logistic regression — 4 Implementing text classification using perception and logistic — 5 Feed-Forward neural networks — 6 Best practices in deep learning — 7 Implementing text classification — 8 Distributional hypothesis and representation learning — 9 Implementing text classification using word — 10 Recurrent neural networks — 11 Implementing part-of-speech tagging using recurrent neural networks — 12 Contextualized embeddings and transformer networks — 13. Using transformers with the hugging face library — 14. Encoder-decoder methods — 15.Implementing Encoder-decoder methods — 16. Neural architectures for natural language processing — Appendix A Overview of the python language and key — Appendix B Character encodings: ASCII and Unicode — References — Index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
"Deep Learning is becoming increasingly important in a technology-dominated world.<br/><br/>However, the building of computational models that accurately represent linguistic structures is complex, as it involves an in-depth knowledge of neural networks, and the understanding of advanced mathematical concepts such as calculus and statistics.<br/><br/>This book makes these complexities accessible to those from a humanities and social sciences background, by providing a clear introduction to deep learning for natural language processing.<br/><br/>It covers both theoretical and practical aspects, and assumes minimal knowledge of machine learning, explaining the theory behind natural language in an easy-to-read way.<br/><br/>It includes pseudo code for the simpler algorithms discussed, and actual Python code for the more complicated architectures, using modern deep learning libraries such as PyTorch and Hugging Face.<br/><br/>Providing the necessary theoretical foundation and practical tools, this book will enable readers to immediately begin building real-world, practical natural language processing systems." --Provided by the publisher |
| 521 ## - TARGET AUDIENCE NOTE |
|---|
| Target audience note |
Adult |
| 541 ## - IMMEDIATE SOURCE OF ACQUISITION NOTE |
|---|
| Source of acquisition |
Published |
| Deans/Chairperson |
Ortega, Eric |
| Department |
College of Computer Studies |
| Subject Category |
Computer Science |
| 546 ## - LANGUAGE NOTE |
|---|
| Language note |
Text in English |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Natural language processing (Computer science). |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Deep learning (Machine learning). |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Neural networks (Computer science). |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computational linguistics. |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Valenzuela-Escárcega, Marco Antonio, |
| Relator term |
author. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Type of record |
Book |
| 998 ## - LOCAL CONTROL INFORMATION (RLIN) |
|---|
| Encoded by |
Janna [new] |
| Date encoded |
07/02/2026 |
