Deep Learning: Methods and Applications
by Deng, Li
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 …
Read More ↓Title Statements
- Full title :Deep Learning : Methods and Applications / Li Deng, Dong Yu.
Summary
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.
Read More ↓Edition Statements
- Publication Date:©2014
Publication Statements
- Publisher(s):Now
- Place of Publication:Boston
- Language:eng
- Publication Date:©2014
The Physical Object
- Description:1 online resource (pages 197-387) : : illustrations (some color)
Edition Identifiers
- ISBN:1601988141, 9781601988140
- oclc:ocn883311885
- LC Classification:Q325.5
- Dewey Decimal:006.31
Table of Contents
1. Introduction -- 1.1 Definitions and background -- 1.2 Organization of this monograph
3. Three classes of deep learning networks -- 3.1 A three-way categorization -- 3.2 Deep networks for unsupervised or generative learning -- 3.3 Deep networks for supervised learning -- 3.4 Hybrid deep networks
4. Deep autoencoders, unsupervised learning -- 4.1 Introduction -- 4.2 Use of deep autoencoders to extract speech features -- 4.3 Stacked denoising autoencoders -- 4.4 Transforming autoencoders
5. Pre-trained deep neural networks, a hybrid -- 5.1 Restricted Boltzmann machines -- 5.2 Unsupervised layer-wise pre-training -- 5.3 Interfacing DNNs with HMMs
6. Deep stacking networks and variants, supervised learning -- 6.1 Introduction -- 6.2 A basic architecture of the deep stacking network -- 6.3 A method for learning the DSN weights -- 6.4 The tensor deep stacking network -- 6.5 The kernelized deep stacking network
7. Selected applications in speech and audio processing -- 7.1 Acoustic modeling for speech recognition -- 7.2 Speech synthesis -- 7.3 Audio and music processing
8. Selected applications in language, modeling and natural language processing -- 8.1 Language modeling -- 8.2 …
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