Python for Natural Language Processing (Record no. 88524)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 05194nam a22006015i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-57549-5 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730172736.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240709s2024 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031575495 |
| -- | 978-3-031-57549-5 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.35 |
| 100 1# - AUTHOR NAME | |
| Author | Nugues, Pierre M. |
| 245 10 - TITLE STATEMENT | |
| Title | Python for Natural Language Processing |
| Sub Title | Programming with NumPy, scikit-learn, Keras, and PyTorch / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 3rd ed. 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XXV, 520 p. 89 illus., 53 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Cognitive Technologies, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Preface to the third edition -- Preface to the second edition -- Preface to the first edition -- 1. An Overview of Language Processing -- 2. A Tour of Python -- 3. Corpus Processing Tools -- 4. Encoding and Annotation Scheme -- 5. Python for Numerical Computations -- 6. Topics in Information Theory and Machine Learning -- 7. Linear and Logistic Regression -- 8. Neural Networks -- 9. Counting and Indexing Words -- 10. Dense Vector Representations -- 11. Word Sequences -- 12. Words, Parts of Speech, and Morphology -- 13. Subword Segmentation -- 14. Part-of-Speech and Sequence Annotation -- 15. Self-Attention and Transformers -- 16. Pretraining an Encoder: The BERT Language Model -- 17. Sequence-to-Sequence Architectures: Encoder-Decoders and Decoders -- Index -- References. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Since the last edition of this book (2014), progress has been astonishing in all areas of Natural Language Processing, with recent achievements in Text Generation that spurred a media interest going beyond the traditional academic circles. Text Processing has meanwhile become a mainstream industrial tool that is used, to various extents, by countless companies. As such, a revision of this book was deemed necessary to catch up with the recent breakthroughs, and the author discusses models and architectures that have been instrumental in the recent progress of Natural Language Processing. As in the first two editions, the intention is to expose the reader to the theories used in Natural Language Processing, and to programming examples that are essential for a deep understanding of the concepts. Although present in the previous two editions, Machine Learning is now even more pregnant, having replaced many of the earlier techniques to process text. Many new techniques build on the availability of text. Using Python notebooks, the reader will be able to load small corpora, format text, apply the models through executing pieces of code, gradually discover the theoretical parts by possibly modifying the code or the parameters, and traverse theories and concrete problems through a constant interaction between the user and the machine. The data sizes and hardware requirements are kept to a reasonable minimum so that a user can see instantly, or at least quickly, the results of most experiments on most machines. The book does not assume a deep knowledge of Python, and an introduction to this language aimed at Text Processing is given in Ch. 2, which will enable the reader to touch all the programming concepts, including NumPy arrays and PyTorch tensors as fundamental structures to represent and process numerical data in Python, or Keras for training Neural Networks to classify texts. Covering topics like Word Segmentation and Part-of-Speech and Sequence Annotation, the textbook also gives an in-depth overview of Transformers (for instance, BERT), Self-Attention and Sequence-to-Sequence Architectures. . |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-57549-5 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer Nature Switzerland : |
| -- | Imprint: Springer, |
| -- | 2024. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Natural language processing (Computer science). |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational linguistics. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Python (Computer program language). |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | User interfaces (Computer systems). |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Human-computer interaction. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Natural Language Processing (NLP). |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational Linguistics. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Python. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | User Interfaces and Human Computer Interaction. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2197-6635 |
| 912 ## - | |
| -- | ZDB-2-SCS |
| 912 ## - | |
| -- | ZDB-2-SXCS |
No items available.