
Deep Learning
Methods and Applications
1st Edition
Published on 30. June 2014
Book
Paperback/Softback
212 pages
978-1-60198-814-0 (ISBN)
Description
Deep Learning: Methods and Applications provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2) the application areas that have already been transformed by the successful use of deep learning technology, such as speech recognition and computer vision; and (3) the application areas that have the potential to be impacted significantly by deep learning and that have been benefitting from recent research efforts, including natural language and text processing, information retrieval, and multimodal information processing empowered by multitask deep learning.
Deep Learning: Methods and Applications is a timely and important book for researchers and students with an interest in deep learning methodology and its applications in signal and information processing.
Deep Learning: Methods and Applications is a timely and important book for researchers and students with an interest in deep learning methodology and its applications in signal and information processing.
More details
Series
Language
English
Place of publication
Hanover
United States
Target group
College/higher education
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 11 mm
Weight
305 gr
ISBN-13
978-1-60198-814-0 (9781601988140)
DOI
10.1561/2000000039
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Content
Endorsement
1. Introduction
2. Some Historical Context of Deep Learning
3. Three Classes of Deep Learning Networks
4. Deep Autoencoders - Unsupervised Learning
5. Pre-Trained Deep Neural Networks - A Hybrid
6. Deep Stacking Networks and Variants - Supervised Learning
7. Selected Applications in Speech and Audio Processing
8. Selected Applications in Language Modeling and Natural Language Processing
9. Selected Applications in Information Retrieval
10. Selected Applications in Object Recognition and Computer Vision
11. Selected Applications in Multimodal and Multi-task Learning
12. Conclusion
References
1. Introduction
2. Some Historical Context of Deep Learning
3. Three Classes of Deep Learning Networks
4. Deep Autoencoders - Unsupervised Learning
5. Pre-Trained Deep Neural Networks - A Hybrid
6. Deep Stacking Networks and Variants - Supervised Learning
7. Selected Applications in Speech and Audio Processing
8. Selected Applications in Language Modeling and Natural Language Processing
9. Selected Applications in Information Retrieval
10. Selected Applications in Object Recognition and Computer Vision
11. Selected Applications in Multimodal and Multi-task Learning
12. Conclusion
References