Deep Learning: Concepts and Architectures

Chapter 1 Deep Learning Architectures

Chapter 2 Theoretical Characterization of Deep Neural Networks

Chapter 3 Scaling Analysis of Specialized Tensor Processing Architectures for Deep Learning Models

Chapter 4 Assessment of Autoencoder Architectures for Data Representation

Chapter 5 The Encoder-Decoder Framework and Its Applications

Chapter 6 Deep Learning for Learning Graph Representations

Chapter 7 Deep Neural Networks for Corrupted Labels

Chapter 8 Constructing a Convolutional Neural Network with a Suitable Capacity for a Semantic Segmentation Task

Chapter 9 Using Convolutional Neural Networks to Forecast Sporting Event Results

Chapter 10 Heterogeneous Computing System for Deep Learning

Chapter 11 Progress in Neural Network Based Statistical Language Modeling