Titlebook: Deep Learning: Fundamentals, Theory and Applications; Kaizhu Huang,Amir Hussain,Rui Zhang Book 2019 Springer Nature Switzerland AG 2019 Ne

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Oceanic Data Analysis with Deep Learning Models,o extract effective information from these raw data becomes an urgent problem in the research of ocean science. In this chapter, we review the data representation learning algorithms, which try to learn effective features from raw data and deliver high prediction accuracy for the unseen data. Partic

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Introduction to Deep Density Models with Latent Variables,ore efficient (sometimes exponentially) than shallow architectures. The performance is evaluated between two shallow models, and two deep models separately on both density estimation and clustering. Furthermore, the deep models are also compared with their shallow counterparts.

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Deep Learning Based Handwritten Chinese Character and Text Recognition, n-gram LMs (BLMs), two types of character-level neural network LMs (NNLMs), namely, feedforward neural network LMs (FNNLMs) and recurrent neural network LMs (RNNLMs) are applied. Both FNNLMs and RNNLMs are combined with BLMs to construct hybrid LMs. To further improve the performance of HCTR, we al

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Deep Learning for Natural Language Processing,onditional Random Fields (Lafferty et al., Conditional random fields: probabilistic models for segmenting and labeling sequence data. In: Proceedings of ICML, 2001) are dominant methods for natural language processing (Manning and Schütze, Foundations of statistical natural language processing. MIT

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Politische Kultur und Sprache im Umbruch hybrid unit is used to encode the long contextual trajectories, which comprise of a BLSTM (bidirectional Long Short-Term Memory) layer and a FFS (feed forward subsampling) layer. Secondly, the CTC (Connectionist Temporal Classification) objective function makes it possible to train the model withou

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Anpassung der ostdeutschen Wirtschaft n-gram LMs (BLMs), two types of character-level neural network LMs (NNLMs), namely, feedforward neural network LMs (FNNLMs) and recurrent neural network LMs (RNNLMs) are applied. Both FNNLMs and RNNLMs are combined with BLMs to construct hybrid LMs. To further improve the performance of HCTR, we al

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