《Deep Learning: Methods and Applications (Foundations and Trends(r) in Signal Processing)》
Deep Learning: Methods and Applications (Foundations and Trends(r) in Signal Processing)txt,chm,pdf,epub,mobi下载
作者: Li Deng / Dong Yu
出版社: Now Publishers Inc
出版年: 2014-6-12
页数: 212
定价: USD 94.05
装帧: Paperback
ISBN: 9781601988140
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
· · · · · · ()
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
· · · · · · ()
作者: Li Deng / Dong Yu
出版社: Now Publishers Inc
出版年: 2014-6-12
页数: 212
定价: USD 94.05
装帧: Paperback
ISBN: 9781601988140
内容简介 · · · · · ·
This book is aimed to provide 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: 1) expertise or knowledge of the authors; 2) the application areas that have already been transformed by the successful use of deep learning technology, su...
作者简介 · · · · · ·
http://research.microsoft.com/en-us/people/deng/
目录 · · · · · ·
1. Introduction2. 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
· · · · · · ()
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
· · · · · · ()
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