000			03367nam a22003497a 4500
003			OSt
005			20260702142954.0
008			260702s20242024si a|||er|||| 001 0 eng d
020			_a9781009012652 [paperback]
040			_aUniversity of Cebu-Banilad _cUniversity of Cebu-Banilad
100			_aSurdeanu, Mihai, _eauthor.
245			_aDeep learning for natural language processing : _ba gentle introduction / _cMihai Surdeanu and Marco Antonio Valezuela-Escárcega.
260			_aSingapore : _bCambridge University Press, _cc2024.
300			_axviii, 325 pages : _billustrations (black and white) ; _c23 cm.
336			_2rdacontent _atext
337			_2rdamedia _aunmediated
338			_2rdacarrier _avolume
504			_aIncludes bibliographical references and index.
505			_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.
520			_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
521			_aAdult
541			_aPublished _xOrtega, Eric _yCollege of Computer Studies _zComputer Science
546			_aText in English
650			_aNatural language processing (Computer science).
650			_aDeep learning (Machine learning).
650			_aNeural networks (Computer science).
650			_aComputational linguistics.
700			_aValenzuela-Escárcega, Marco Antonio, _eauthor.
942			_2ddc _cBK
998			_cJanna [new] _d07/02/2026
999			_c15453 _d15453