MARC details
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| fixed length control field |
02608nam a22002417a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20220808101646.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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220629b ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9783030145989 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.35 |
| Item number |
KAM |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Kamath, Uday |
| 245 ## - TITLE STATEMENT |
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| Title |
Deep learning for NLP and speech recognition |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc. |
Springer |
| Place of publication, distribution, etc. |
Switzerland |
| Date of publication, distribution, etc. |
2019 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
xxvi, 621 p. |
| 365 ## - TRADE PRICE |
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| Price type code |
EURO |
| Price amount |
79.99 |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc. |
About this book<br/>With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. <br/><br/>The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are:<br/> Machine Learning, NLP, and Speech Introduction<br/><br/>The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries.<br/> Deep Learning Basics<br/><br/>The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks.<br/><br/> Advanced Deep Learning Techniques for Text and Speech<br/><br/>The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Automatic speech recognition |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Natural language processing (Computer science) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Python (Computer program language) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Artificial intelligence |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Liu, John |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Whitaker, James |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |