Deep Learning: Fundamentals, Theory and Applications

  • Kaizhu Huang
  • Amir Hussain
  • Qiu-Feng Wang
  • Rui Zhang

Part of the Cognitive Computation Trends book series (COCT, volume 2)

Table of contents

  1. Front Matter
    Pages i-vii
  2. Xi Yang, Kaizhu Huang, Rui Zhang, Amir Hussain
    Pages 1-29
  3. Tonghua Su, Li Sun, Qiu-Feng Wang, Da-Han Wang
    Pages 31-55
  4. Xu-Yao Zhang, Yi-Chao Wu, Fei Yin, Cheng-Lin Liu
    Pages 57-88
  5. Haiqin Yang, Linkai Luo, Lap Pong Chueng, David Ling, Francis Chin
    Pages 89-109
  6. Jiajun Zhang, Chengqing Zong
    Pages 111-138
  7. Guoqiang Zhong, Li-Na Wang, Qin Zhang, Estanislau Lima, Xin Sun, Junyu Dong et al.
    Pages 139-160
  8. Back Matter
    Pages 161-163

About this book


The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing.

Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field.

This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.


Neural networks Deep representation Learning Optimization Artificial intelligence Cognitively-inspired methods

Editors and affiliations

  • Kaizhu Huang
    • 1
  • Amir Hussain
    • 2
  • Qiu-Feng Wang
    • 3
  • Rui Zhang
    • 4
  1. 1.Xi’an Jiaotong-Liverpool UniversitySuzhouChina
  2. 2.School of ComputingEdinburgh Napier UniversityEdinburghUK
  3. 3.Xi’an Jiaotong-Liverpool UniversitySuzhouChina
  4. 4.Xi’an Jiaotong-Liverpool UniversitySuzhouChina

Bibliographic information

  • DOI
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Biomedical and Life Sciences
  • Print ISBN 978-3-030-06072-5
  • Online ISBN 978-3-030-06073-2
  • Series Print ISSN 2524-5341
  • Series Online ISSN 2524-535X
  • Buy this book on publisher's site