Atjaunināt sīkdatņu piekrišanu

E-grāmata: Deep Learning for Natural Language Processing: A Gentle Introduction

(University of Arizona), (University of Arizona)
  • Formāts: PDF+DRM
  • Izdošanas datums: 08-Feb-2024
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781009027991
Citas grāmatas par šo tēmu:
  • Formāts - PDF+DRM
  • Cena: 35,68 €*
  • * ši ir gala cena, t.i., netiek piemērotas nekādas papildus atlaides
  • Ielikt grozā
  • Pievienot vēlmju sarakstam
  • Šī e-grāmata paredzēta tikai personīgai lietošanai. E-grāmatas nav iespējams atgriezt un nauda par iegādātajām e-grāmatām netiek atmaksāta.
  • Formāts: PDF+DRM
  • Izdošanas datums: 08-Feb-2024
  • Izdevniecība: Cambridge University Press
  • Valoda: eng
  • ISBN-13: 9781009027991
Citas grāmatas par šo tēmu:

DRM restrictions

  • Kopēšana (kopēt/ievietot):

    nav atļauts

  • Drukāšana:

    nav atļauts

  • Lietošana:

    Digitālo tiesību pārvaldība (Digital Rights Management (DRM))
    Izdevējs ir piegādājis šo grāmatu šifrētā veidā, kas nozīmē, ka jums ir jāinstalē bezmaksas programmatūra, lai to atbloķētu un lasītu. Lai lasītu šo e-grāmatu, jums ir jāizveido Adobe ID. Vairāk informācijas šeit. E-grāmatu var lasīt un lejupielādēt līdz 6 ierīcēm (vienam lietotājam ar vienu un to pašu Adobe ID).

    Nepieciešamā programmatūra
    Lai lasītu šo e-grāmatu mobilajā ierīcē (tālrunī vai planšetdatorā), jums būs jāinstalē šī bezmaksas lietotne: PocketBook Reader (iOS / Android)

    Lai lejupielādētu un lasītu šo e-grāmatu datorā vai Mac datorā, jums ir nepieciešamid Adobe Digital Editions (šī ir bezmaksas lietotne, kas īpaši izstrādāta e-grāmatām. Tā nav tas pats, kas Adobe Reader, kas, iespējams, jau ir jūsu datorā.)

    Jūs nevarat lasīt šo e-grāmatu, izmantojot Amazon Kindle.

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.

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.

Recenzijas

'A wonderful introduction to natural language processing, emphasizing the machine learning fundamentals. The authors perfectly interleave theory with chapters giving practical implementations using PyTorch, and make it all seem easy, with a warm tone and clear and well-structured explanations. This book is a delight!' Dan Jurafsky, Professor of Linguistics and Computer Science, Stanford University 'Recommended to all readers interested in this area, especially upper-level undergraduate and graduate students, researchers, faculty, and professionals.' C. Tappert, Choice

Papildus informācija

Provides a clear, accessible introduction to deep learning for natural language processing, covering both practical and theoretical aspects.
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.
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. Marco Antonio Valenzuela-Escįrcega is a Research Scientist in the Computer Science department at the University of Arizona. He has worked on natural language processing projects in both industry and academia.