03367nam a22003497a 4500 OSt 20260702142954.0 260702s20242024si a|||er|||| 001 0 eng d 9781009012652 [paperback] University of Cebu-Banilad University of Cebu-Banilad Surdeanu, Mihai, author. Deep learning for natural language processing : a gentle introduction / Mihai Surdeanu and Marco Antonio Valezuela-Escárcega. Singapore : Cambridge University Press, c2024. xviii, 325 pages : illustrations (black and white) ; 23 cm. rdacontent text rdamedia unmediated rdacarrier volume Includes bibliographical references and index. 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. "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 Adult Published Ortega, Eric College of Computer Studies Computer Science Text in English Natural language processing (Computer science). Deep learning (Machine learning). Neural networks (Computer science). Computational linguistics. Valenzuela-Escárcega, Marco Antonio, author. ddc BK Janna [new] 07/02/2026 15453 15453 0 0 ddc 0 0 UCBL_MAIN UCBL_MAIN SUBJ.REF 2026-07-02 ALBASA-Mindmover 5998.00 0 006.35 Su77 2024 3UCBL000029693 2026-07-02 2026-07-02 SR