Handbook of Deep Learning Applications

Description

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data.

As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and PhD. scholars.

Table of Contents

1. Front Matter
2. Designing a Neural Network from Scratch for Big Data Powered by Multi-node GPUs
3. Deep Learning for Scene Understanding
4. An Application of Deep Learning in Character Recognition: An Overview
5. Deep Learning for Driverless Vehicles
6. Deep Learning for Document Representation
7. Applications of Deep Learning in Medical Imaging
8. Deep Learning for Marine Species Recognition
9. Deep Molecular Representation in Cheminformatics
10. A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Driving
11. Phase Identification and Workflow Modeling in Laparoscopy Surgeries Using Temporal Connectionism of Deep Visual Residual Abstractions
12. Deep Learning Applications to Cytopathology: A Study on the Detection of Malaria and on the Classification of Leukaemia Cell-Lines
13. Application of Deep Neural Networks for Disease Diagnosis Through Medical Data Sets
14. Why Dose Layer-by-Layer Pre-training Improve Deep Neural Networks Learning?
15. Springer: Deep Learning in eHealth
16. Deep Learning for Brain Computer Interfaces
17. Reducing Hierarchical Deep Learning Networks as Game Playing Artefact Using Regret Matching
18. Deep Learning in Gene Expression Modeling

Editors Biography

Valentina Emilia Balas
Aurel Vlaicu University of Arad, Arad, Romania

Sanjiban Sekhar Roy
School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

Dharmendra Sharma
University of Canberra, Bruce, Australia

Pijush Samui
Department of Civil Engineering, National Institute of Technology Patna, Patna, India