Deep Learning: Fundamentals, Theory and Applications
Herausgeber: Huang, K., Hussain, A., Wang, Q.-F., Zhang, R. (Eds.)
Vorschau- Provides thorough background of deep learning
- Introduces widely-used learning architectures and algorithms
- Includes new theory and applications of deep learning
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- Über dieses Buch
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The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing.
Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field.
This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.
- Stimmen zum Buch
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“This reviewer maintains skepticism about how accessible this book is to the typical undergraduate. However, a senior level graduate student may find incredible value in the exposition. The practitioner may enjoy this text as a companion to an existing library as well as a muse for modifying current methodologies by those cited in the research papers.” (Mannan Shah, MAA Reviews, September 22, 2019)
- Inhaltsverzeichnis (6 Kapitel)
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Introduction to Deep Density Models with Latent Variables
Seiten 1-29
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Deep RNN Architecture: Design and Evaluation
Seiten 31-55
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Deep Learning Based Handwritten Chinese Character and Text Recognition
Seiten 57-88
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Deep Learning and Its Applications to Natural Language Processing
Seiten 89-109
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Deep Learning for Natural Language Processing
Seiten 111-138
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Inhaltsverzeichnis (6 Kapitel)
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Bibliografische Information
- Bibliographic Information
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- Buchtitel
- Deep Learning: Fundamentals, Theory and Applications
- Herausgeber
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- Kaizhu Huang
- Amir Hussain
- Qiu-Feng Wang
- Rui Zhang
- Titel der Buchreihe
- Cognitive Computation Trends
- Buchreihen Band
- 2
- Copyright
- 2019
- Verlag
- Springer International Publishing
- Copyright Inhaber
- Springer Nature Switzerland AG
- eBook ISBN
- 978-3-030-06073-2
- DOI
- 10.1007/978-3-030-06073-2
- Hardcover ISBN
- 978-3-030-06072-5
- Buchreihen ISSN
- 2524-5341
- Auflage
- 1
- Seitenzahl
- VII, 163
- Anzahl der Bilder
- 20 schwarz-weiß Abbildungen, 46 Abbildungen in Farbe
- Themen
