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Handbook of Deep Learning Applications

  • Book
  • © 2019

Overview

Editors:
  1. Valentina Emilia Balas

    1. Aurel Vlaicu University of Arad, Arad, Romania

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  2. Sanjiban Sekhar Roy

    1. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

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  3. Dharmendra Sharma

    1. University of Canberra, Bruce, Australia

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  4. Pijush Samui

    1. Department of Civil Engineering, National Institute of Technology Patna, Patna, India

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  • Provides a concise and structured presentation of deep learning applications
  • Introduces a large range of applications related to vision, speech, and natural language processing
  • Includes active research trends, challenges, and future directions of deep learning

Part of the book series: Smart Innovation, Systems and Technologies (SIST, volume 136)

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-vi
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  2. Designing a Neural Network from Scratch for Big Data Powered by Multi-node GPUs

    • Alcides Fonseca, Bruno Cabral
    Pages 1-19

  3. Deep Learning for Scene Understanding

    • Uzair Nadeem, Syed Afaq Ali Shah, Ferdous Sohel, Roberto Togneri, Mohammed Bennamoun
    Pages 21-51

  4. An Application of Deep Learning in Character Recognition: An Overview

    • Sana Saeed, Saeeda Naz, Muhammad Imran Razzak
    Pages 53-81

  5. Deep Learning for Driverless Vehicles

    • Cameron Hodges, Senjian An, Hossein Rahmani, Mohammed Bennamoun
    Pages 83-99

  6. Deep Learning for Document Representation

    • Mehran Kamkarhaghighi, Eren Gultepe, Masoud Makrehchi
    Pages 101-110

  7. Applications of Deep Learning in Medical Imaging

    • Sanjit Maitra, Ratul Ghosh, Kuntal Ghosh
    Pages 111-127

  8. Deep Learning for Marine Species Recognition

    • Lian Xu, Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid
    Pages 129-145

  9. Deep Molecular Representation in Cheminformatics

    • Peng Jiang, Serkan Saydam, Hamed Lamei Ramandi, Alan Crosky, Mojtaba Maghrebi
    Pages 147-159

  10. A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Driving

    • ÇaÄŸrı Kaymak, AyÅŸegül Uçar
    Pages 161-200

  11. Phase Identification and Workflow Modeling in Laparoscopy Surgeries Using Temporal Connectionism of Deep Visual Residual Abstractions

    • Kaustuv Mishra, Rachana Sathish, Debdoot Sheet
    Pages 201-217

  12. Deep Learning Applications to Cytopathology: A Study on the Detection of Malaria and on the Classification of Leukaemia Cell-Lines

    • G. Gopakumar, Gorthi R. K. Sai Subrahmanyam
    Pages 219-257

  13. Application of Deep Neural Networks for Disease Diagnosis Through Medical Data Sets

    • Alper BaÅŸtürk, Hasan Badem, Abdullah Caliskan, Mehmet Emin Yüksel
    Pages 259-292

  14. Why Dose Layer-by-Layer Pre-training Improve Deep Neural Networks Learning?

    • Seyyede Zohreh Seyyedsalehi, Seyyed Ali Seyyedsalehi
    Pages 293-318

  15. Springer: Deep Learning in eHealth

    • Peter Wlodarczak
    Pages 319-331

  16. Deep Learning for Brain Computer Interfaces

    • Ankita Bose, Sanjiban Sekhar Roy, Valentina Emilia Balas, Pijush Samui
    Pages 333-344

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

    • Arindam Chaudhuri, Soumya K. Ghosh
    Pages 345-362

  18. Deep Learning in Gene Expression Modeling

    • Dinesh Kumar, Dharmendra Sharma
    Pages 363-383
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Keywords

About this 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.

Editors and Affiliations

  • Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  • School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

    Sanjiban Sekhar Roy

  • University of Canberra, Bruce, Australia

    Dharmendra Sharma

  • Department of Civil Engineering, National Institute of Technology Patna, Patna, India

    Pijush Samui

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