文件名称:
Deep Learning Methods and Applications
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文件大小: 8mb
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上传时间: 2015-05-20
详细说明： 1 Introduction 198 1.1 Definitions and background . . . . . . . . . . . . . . . . . 198 1.2 Organization of this monograph . . . . . . . . . . . . . . 202 2 Some Historical Context of Deep Learning 205 3 Three Classes of Deep Learning Networks 214 3.1 A three-way categorization . . . . . . . . . . . . . . . . . 214 3.2 Deep networks for unsupervised or generative learning . . . 216 3.3 Deep networks for supervised learning . . . . . . . . . . . 223 3.4 Hybrid deep networks . . . . . . . . . . . . . . . . . . . . 226 4 Deep Autoe ncoders — Unsupervised Learning 230 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 230 4.2 Use of deep autoencoders to extract speech features . . . 231 4.3 Stacked denoising autoencoders . . . . . . . . . . . . . . . 235 4.4 Transforming autoencoders . . . . . . . . . . . . . . . . . 239 5 Pre-Trained Deep Neural Networks — A Hybrid 241 5.1 Restricted Boltzmannmachines . . . . . . . . . . . . . . . 241 5.2 Unsupervised layer-wise pre-training . . . . . . . . . . . . 245 5.3 Interfacing DNNs with HMMs . . . . . . . . . . . . . . . 248 6 Deep Stacking Networks and Variants — Supervised Learning 250 6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . 250 6.2 A basic architecture of the deep stacking network . . . . . 252 6.3 A method for learning the DSN weights . . . . . . . . . . 254 6.4 The tensor deep stacking network . . . . . . . . . . . . . . 255 6.5 The Kernelized deep stacking network . . . . . . . . . . . 257 7 Selected Applications in Speech and Audio Processing 262 7.1 Acoustic modeling for speech recognition . . . . . . . . . . 262 7.2 Speech synthesis . . . . . . . . . . . . . . . . . . . . . . . 286 7.3 Audio and music processing . . . . . . . . . . . . . . . . . 288 8 Selected Applications in Language Modeling and Natural Language Processing 292 8.1 Language modeling . . . . . . . . . . . . . . . . . . . . . 293 8.2 Natural language processing . . . . . . . . . . . . . . . . . 299 9 Selected Applications in Information Retrieval 308 9.1 A brief introduction to information retrieval . . . . . . . . 308 9.2 SHDA for document indexing and retrieval . . . . . . . . . 310 9.3 DSSMfor document retrieval . . . . . . . . . . . . . . . . 311 9.4 Use of deep stacking networks for information retrieval . . 317 10 Selected Applications in Object Recognition and Computer Vision 320 10.1 Unsupervised or generative feature learning . . . . . . . . 321 10.2 Supervised feature learning and classification . . . . . . . . 324 11 Selected Applications in Multimodal and Multi-task Learning 331 11.1 Multi-modalities: Text and image . . . . . . . . . . . . . . 332 11.2 Multi-modalities: Speech and image . . . . . . . . . . . . 336 11.3 Multi-task learning within the speech, NLP or image . . . 339 12 Conclusion 343 References 349 ...展开收缩
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