Mihai Surdeanu (University of Arizona), Marco Antonio Valenzuela-Escarcega (University of Arizona)

Deep Learning for Natural Language Processing

• Broschiertes Buch

Produktbeschreibung

A clear, accessible introduction to deep learning for natural language processing (NLP), this book is ideal for readers without a background in machine learning and NLP. It covers the necessary theoretical context using minimal jargon also covers practical aspects, using actual Python code for the neural architectures discussed.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 344
• Erscheinungstermin: 12. Januar 2024
• Englisch
• Abmessung: 229mm x 152mm x 19mm
• Gewicht: 510g
• ISBN-13: 9781009012652
• ISBN-10: 1009012657
• Artikelnr.: 69037680

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.