03486nam a22003497a 4500003000400000005001700004008004100021020003000062040005900092100003100151245013800182260005500320300006900375336002200444337002500466338002400491504005100515505091300566520111701479521001102596541007502607546002102682650005302703650003902756650004102795650003202836700005402868942001202922998002802934999001702962952015702979OSt20260702142954.0260702s20242024si a|||er|||| 001 0 eng d  a9781009012652 [paperback]  aUniversity of Cebu-BaniladcUniversity of Cebu-Banilad  aSurdeanu, Mihai, eauthor.  aDeep learning for natural language processing :  ba gentle introduction / cMihai Surdeanu and Marco Antonio Valezuela-Escárcega.   aSingapore : bCambridge University Press, cc2024.  axviii, 325 pages :  billustrations (black and white) ;c23 cm.   2rdacontentatext   2rdamediaaunmediated  2rdacarrieravolume   aIncludes bibliographical references and index.  aContents: 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.  a"Deep Learning is becoming increasingly important in a technology-dominated world.

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.

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.

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.

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.

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   aAdult   aPublishedxOrtega, EricyCollege of Computer StudieszComputer Science  aText in English   aNatural language processing (Computer science).   aDeep learning (Machine learning).   aNeural networks (Computer science).   aComputational linguistics.   aValenzuela-Escárcega, Marco Antonio, eauthor.   2ddccBK  cJanna [new]d07/02/2026  c15453d15453  00102ddc4070aUCBL_MAINbUCBL_MAINcSUBJ.REFd2026-07-02eALBASA-Mindmover g5998.00l0o006.35 Su77 2024p3UCBL000029693r2026-07-02w2026-07-02ySR