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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.
Machine Learning, NLP, and Speech Introduction
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.Deep Learning Basics
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.
Advanced Deep Learning Techniques for Text and Speech
| Autorius: | Uday Kamath, John Liu, James Whitaker, |
| Leidėjas: | Springer-Verlag GmbH |
| Išleidimo metai: | 2020 |
| ISBN-10: | 3030145980 |
| ISBN-13: | 9783030145989 |
| Formatas: | 251 x 176 x 37 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
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