Titlebook: Deep Learning for NLP and Speech Recognition; Uday Kamath,John Liu,James Whitaker Textbook 2019 Springer Nature Switzerland AG 2019 Deep L

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Distributed Representationshow it can be leveraged to form semantic representations of words. We discuss the common distributional semantic models including . and . and their variants. We address the shortcomings of embedding models and their extension to document and concept representation. Finally, we discuss several applic

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Convolutional Neural Networksucting complex deep learning solutions for various NLP, speech, and time series tasks. LeCun first introduced certain basic parts of the CNN frameworks as a general NN framework to solve various high-dimensional data problems in computer vision, speech, and time series. ImageNet applied convolutions

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Attention and Memory Augmented Networks forms of convolutional and recurrent networks, respectively. When the data has certain dependencies such as out-of-order access, long-term dependencies, unordered access, most standard architectures discussed are not suitable. Let us consider a specific example from the bAbI dataset where there are

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tectures.A ready reference for deep learning techniques applThis textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition.?With the widespread adoption of deep learning, nat