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Deep Learning: Concepts and Architectures

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Cover of 'Deep Learning: Concepts and Architectures'

Table of Contents

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    Book Overview
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    Chapter 1 Deep Learning Architectures
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    Chapter 2 Theoretical Characterization of Deep Neural Networks
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    Chapter 3 Scaling Analysis of Specialized Tensor Processing Architectures for Deep Learning Models
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    Chapter 4 Assessment of Autoencoder Architectures for Data Representation
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    Chapter 5 The Encoder-Decoder Framework and Its Applications
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    Chapter 6 Deep Learning for Learning Graph Representations
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    Chapter 7 Deep Neural Networks for Corrupted Labels
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    Chapter 8 Constructing a Convolutional Neural Network with a Suitable Capacity for a Semantic Segmentation Task
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    Chapter 9 Using Convolutional Neural Networks to Forecast Sporting Event Results
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    Chapter 10 Heterogeneous Computing System for Deep Learning
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    Chapter 11 Progress in Neural Network Based Statistical Language Modeling
Attention for Chapter 5: The Encoder-Decoder Framework and Its Applications
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