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Handbook of Deep Learning Applications - Research Book

Handbook of Deep Learning Applications - Research Book

Essential Research Book in Handbook of Deep Learning Applications

Author(s) Name:  Valentina Emilia Balas, Sanjiban Sekhar Roy, Dharmendra Sharma, Pijush Samui

About the Book:

   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.

Table of Contents

  • 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
  • Reducing Hierarchical Deep Learning Networks as Game Playing Artefact Using Regret Matching

    • ISBN:  978-3-030-11479-4

    Publisher:   Springer

    Year of Publication:  2019

    Book Link:  Home Page Url