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Handbook of Deep Learning Applications

  • Valentina Emilia Balas
  • Sanjiban Sekhar Roy
  • Dharmendra Sharma
  • Pijush Samui

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

Table of contents

  1. Front Matter
    Pages i-vi
  2. Uzair Nadeem, Syed Afaq Ali Shah, Ferdous Sohel, Roberto Togneri, Mohammed Bennamoun
    Pages 21-51
  3. Sana Saeed, Saeeda Naz, Muhammad Imran Razzak
    Pages 53-81
  4. Cameron Hodges, Senjian An, Hossein Rahmani, Mohammed Bennamoun
    Pages 83-99
  5. Mehran Kamkarhaghighi, Eren Gultepe, Masoud Makrehchi
    Pages 101-110
  6. Sanjit Maitra, Ratul Ghosh, Kuntal Ghosh
    Pages 111-127
  7. Lian Xu, Mohammed Bennamoun, Senjian An, Ferdous Sohel, Farid Boussaid
    Pages 129-145
  8. Peng Jiang, Serkan Saydam, Hamed Lamei Ramandi, Alan Crosky, Mojtaba Maghrebi
    Pages 147-159
  9. Alper Baştürk, Hasan Badem, Abdullah Caliskan, Mehmet Emin Yüksel
    Pages 259-292
  10. Seyyede Zohreh Seyyedsalehi, Seyyed Ali Seyyedsalehi
    Pages 293-318
  11. Peter Wlodarczak
    Pages 319-331
  12. Ankita Bose, Sanjiban Sekhar Roy, Valentina Emilia Balas, Pijush Samui
    Pages 333-344
  13. Dinesh Kumar, Dharmendra Sharma
    Pages 363-383

About this book

Introduction

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.

Keywords

Deep Machine Learning Deep Neural Network Deep Belief Network Restricted Boltzmann Machine Convolution Neural Network Auto Encoder Big Data Speech Recognition Natural Language Processing

Editors and affiliations

  • Valentina Emilia Balas
    • 1
  • Sanjiban Sekhar Roy
    • 2
  • Dharmendra Sharma
    • 3
  • Pijush Samui
    • 4
  1. 1.Aurel Vlaicu University of AradAradRomania
  2. 2.School of Computer Science and EngineeringVellore Institute of TechnologyVelloreIndia
  3. 3.University of CanberraBruceAustralia
  4. 4.Department of Civil EngineeringNational Institute of Technology PatnaPatnaIndia

Bibliographic information