Handbook of Deep Learning Applications

Handbook of Deep Learning Applications
  • eBook:
    Handbook of Deep Learning Applications
  • Author:
    Valentina Emilia Balas, Sanjiban Sekhar Roy, Dharmendra Sharma, Pijush Samui
  • Edition:
    1st ed. 2019 edition
  • Categories:
  • Data:
    February 26, 2019
  • ISBN:
  • ISBN-13:
  • Language:
  • Pages:
    383 pages
  • Format:
    PDF, ePUB

Book 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 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
Deep Molecular Representation in Cheminformatics
A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Driving
Phase Identification and Workflow Modeling in Laparoscopy Surgeries Using Temporal Connectionism of Deep Visual Residual Abstractions
Deep Learning Applications to Cytopathology: A Study on the Detection of Malaria and on the Classification of Leukaemia Cell-Lines
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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