Название: Handbook of Deep Learning Applications Автор: Valentina Emilia Balas, Sanjiban Sekhar Roy Издательство: Springer ISBN: 3030114783 Год: 2019 Страниц: 380 Язык: английский Формат: pdf (true) Размер: 13.1 MB
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 Ph.D. scholars.
Designing a Neural Network from Scratch for Big Data Powered by Multi-node GPUs Deep Learning for Scene Understanding An Application of Deep Learning in Character Recognition: An Overview Deep Learning for Driverless Vehicles Deep Learning for Document Representation Applications of Deep Learning in Medical Imaging Deep Learning for Marine Species Recognition A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Driving Application of Deep Neural Networks for Disease Diagnosis Through Medical Data Sets Why Dose Layer-by-Layer Pre-training Improve Deep Neural Networks Learning? Springer: Deep Learning in eHealth Deep Learning for Brain Computer Interfaces Reducing Hierarchical Deep Learning Networks as Game Playing Artefact Using Regret Matching Deep Learning in Gene Expression Modeling
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Handbook of Deep Learning Applications 
