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Foundations and Trends® in Signal Processing > Vol 7 > Issue 3–4

Deep Learning: Methods and Applications

By Li Deng, Microsoft Research One Microsoft Way, USA, deng@microsoft.com | Dong Yu, Microsoft Research One Microsoft Way, USA, Dong.Yu@microsoft.com

 
Suggested Citation
Li Deng and Dong Yu (2014), "Deep Learning: Methods and Applications", Foundations and Trends® in Signal Processing: Vol. 7: No. 3–4, pp 197-387. http://dx.doi.org/10.1561/2000000039

Publication Date: 30 Jun 2014
© 2014 L. Deng and D. Yu
 
Subjects
Speech and spoken language processing,  Statistical/Machine learning,  Audio signal processing,  Architectures for IR
 
Keywords
Deep learning, Machine learning, Artificial intelligence, Neural networks, Deep neural networks, Deep stacking networks, Autoencoders, Supervised learning, Unsupervised learning, Hybrid deep networks, Object recognition, Computer vision, Natural language processing, Language models, Multi-task learning, Multi-modal processing
 

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In this article:
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 

Abstract

This monograph 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 experiencing research growth, including natural language and text processing, information retrieval, and multimodal information processing empowered by multi-task deep learning.

DOI:10.1561/2000000039

Book details

ISBN: 978-1-60198-814-0
199 pp. $99.00
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ISBN: 978-1-60198-815-7
199 pp. $240.00
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Table of contents:
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

Deep Learning

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.

"This book provides an overview of a sweeping range of up-to-date deep learning methodologies and their application to a variety of signal and information processing tasks, including not only automatic speech recognition (ASR), but also computer vision, language modeling, text processing, multimodal learning, and information retrieval. This is the first and the most valuable book for "deep and wide learning" of deep learning, not to be missed by anyone who wants to know the breathtaking impact of deep learning on many facets of information processing, especially ASR, all of vital importance to our modern technological society." – Sadaoki Furui, President of Toyota Technological Institute at Chicago, and Professor at the Tokyo Institute of Technology

 
SIG-039