Mihai Surdeanu, Marco Antonio Valenzuela-Escárcega

Deep Learning for Natural Language Processing

• Gebundenes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 344
• Erscheinungstermin: 12. Januar 2024
• Englisch
• Abmessung: 235mm x 157mm x 23mm
• Gewicht: 650g
• ISBN-13: 9781316515662
• ISBN-10: 1316515664
• Artikelnr.: 69038413

Autorenporträt

Mihai Surdeanu is Associate Professor in the Computer Science Department at the University of Arizona. He works in both academia and industry on NLP systems that process and extract meaning from natural language.

Inhaltsangabe

Preface
1. Introduction
2. The perception
3. Logistic regression
4. Implementing text classfication using perceptron and LR
5. Feed forward neural networks
6. Best practices in deep learning
7. Implementing text classification with feed forward networks
8. Distributional hypothesis and representation learning
9. Implementing text classification using word embedding
10. Recurrent neural networks
11. Implementing POS tagging using RNNs
12. Contexualized embeddings and transformer networks
13. Using transformers with the hugging face library
14. Encoder-decoder methods
15. Implementing encoder-decoder methods
16. Neural architecture for NLP applications
Appendix A: Overview of the python language and the key libraries
Appendix B: Character endcodings: ASCII and unicode.