Wei Qi Yan - Computational Methods for Deep Learning: Theoretic, Practice and Applications (Texts in Computer Science)

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This book was drafted based on my recent lectures, talks, and seminars for our postgraduate students at the Auckland University of Technology (AUT), New Zealand. We integrate the materials of deep learning and machine learning as well as artificial neural networks together, refine the content, and publish this book so that more postgraduate students, especially those students who are working for their theses can benefit from our research and teaching work for the purpose of enlightening their projects.

In this book, we organize our stuff and tell our story from easy to difficult in mathematics; we prepare our contents for knowledge transfer from the viewpoint of machine intelligence. We start from understanding artificial neural networks with the design of neurons and the activation functions, then explain the mechanism of deep learning using advanced mathematics. At the end of each chapter, we especially emphasize on how to use Python-based platforms and the latest MATLAB toolboxes for implementing the deep learning algorithms; we also list the questions we concern for thinking and discussion.

Before reading this book, we strongly encourage our readers to learn the knowledge of postgraduate mathematics, especially those fundamental subjects like mathematical analysis, linear algebra, optimizations, computational methods, differential geometry, manifold, information theory as well as basic algebra, functional analysis, graphical models, etc. The computational knowledge will assist us in understanding not only this book but also relevant journal articles and conference papers in the field of deep learning.

This book was written for research students and engineers as well as computer scientists who are interested in computational approaches of deep learning for theoretic analysis and practical development. More generally, this book is also apt for those researchers who are interested in machine intelligence, pattern analysis, computer vision, Natural Language Processing (NLP), and robotics.

Thanks to our peer colleagues and students whose materials were referenced and who have given invaluable comments on this book. Special thanks to my supervised students: Mr. J. Wang, Dr. Y. Zhang, Mr. J. Lu, Mr. D. Shen, Mr. K. Zheng, Ms. Y. Ren, Mr. R. Li, Mr. P. Li, Mr. Z. Liu, Ms. Y. Shen, Ms. H. Wang, Mr. C. Xin, Ms. Q. Zhang, Mr. C. Liu, Ms. B. Xiao, Ms. X. Liu, Mr. C. Song, Mr. X. Ma, Mr. S. Sun, Ms. Y. Fu, Ms. N. An, Ms. L. Zhang, Dr. Q. Gu, my colleagues Dr. M. Nguyen, Prof. R. Klette.