Handbook of Deep Learning Applications

DescriptionProvides a concise and structured presentation of deep learning applicationsIntroduces a large range of applications related to vision, speech, and natural language processingIncludes active research trends, challenges, and future directions of deep learningThis 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 ContentsFront MatterDesigning a Neural Network from Scratch for Big Data Powered by Multi-node GPUsDeep Learning for Scene UnderstandingAn Application of Deep Learning in Character Recognition: An OverviewDeep Learning for Driverless VehiclesDeep Learning for Document RepresentationApplications of Deep Learning in Medical ImagingDeep Learning for Marine Species RecognitionDeep Molecular Representation in CheminformaticsA Brief Survey and an Application of Semantic Image Segmentation for Autonomous DrivingPhase Identification and Workflow Modeling in Laparoscopy Surgeries Using Temporal Connectionism of Deep Visual Residual AbstractionsDeep Learning Applications to Cytopathology: A Study on the Detection of Malaria and on the Classification of Leukaemia Cell-LinesApplication of Deep Neural Networks for Disease Diagnosis Through Medical Data SetsWhy Dose Layer-by-Layer Pre-training Improve Deep Neural Networks Learning?Springer: Deep Learning in eHealthDeep Learning for Brain Computer InterfacesReducing Hierarchical Deep Learning Networks as Game Playing Artefact Using Regret MatchingDeep Learning in Gene Expression ModelingEditors BiographyValentina Emilia Balas Aurel Vlaicu University of Arad, Arad, RomaniaSanjiban Sekhar Roy School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, IndiaDharmendra Sharma University of Canberra, Bruce, AustraliaPijush Samui Department of Civil Engineering, National Institute of Technology Patna, Patna, India