Deep Learning: Fundamentals, Theory and Applications

ISBN-13: 9783030060725 / Angielski / Twarda / 2019 / 163 str.

Kaizhu Huang; Amir Hussain; Qiu-Feng Wang

Recenzje

"This reviewer maintains skepticism about how accessible this book is to the typical undergraduate. However, a senior level graduate student may find incredible value in the exposition. The practitioner may enjoy this text as a companion to an existing library as well as a muse for modifying current methodologies by those cited in the research papers." (Mannan Shah, MAA Reviews, September 22, 2019)

Spis treści

Preface

1.      Introduction to Deep Density Models with Latent Variables
Xi Yang, Kaizhu Huang, Rui Zhang, and Amir Hussain

2.     Deep RNN Architecture: Design and Evaluation
Tonghua Su, Li Sun, Qiu-Feng Wang, and Da-Han Wang 

3.   Deep Learning Based Handwritten Chinese Character and Text Recognition
Xu-Yao Zhang, Yi-Chao Wu, Fei Yin, and Cheng-Lin Liu 

4.      Deep Learning and Its Applications to Natural Language Processing
Haiqin Yang, Linkai Luo, Lap Pong Chueng, David Ling, and Francis Chin

5.      Deep Learning for Natural Language Processing
Jiajun Zhang and Chengqing Zong

6.     Oceanic Data Analysis with Deep Learning Models
Guoqiang Zhong, Li-Na Wang, Qin Zhang, Estanislau Lima, Xin Sun, Junyu Dong, Hui Wang, and Biao Shen

Index.

Blurb

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.