Computational Methods for Deep Learning / Texts in Computer Science (PDF)
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Integrating concepts from deep learning, machine learning, and artificial neural networks, this highly unique textbook presents content progressively from easy to more complex, orienting its content about knowledge transfer from the viewpoint of machine intelligence. It adopts the methodology from graphical theory, mathematical models, and algorithmic implementation, as well as covers datasets preparation, programming, results analysis and evaluations.
Beginning with a grounding about artificial neural networks with neurons and the activation functions, the work then explains the mechanism of deep learning using advanced mathematics. In particular, it emphasizes how to use TensorFlow and the latest MATLAB deep-learning toolboxes for implementing deep learning algorithms.
As a prerequisite, readers should have a solid understanding especially of mathematical analysis, linear algebra, numerical analysis, optimizations, differential geometry, manifold, and information theory, as well as basic algebra, functional analysis, and graphical models. This computational knowledge will assist in comprehending the subject matter not only of this text/reference, but also in relevant deep learning journal articles and conference papers.
This textbook/guide is aimed at Computer Science research students and engineers, as well as scientists interested in deep learning for theoretic research and analysis. More generally, this book is also helpful for those researchers who are interested in machine intelligence, pattern analysis, natural language processing, and machine vision.
Dr. Wei Qi Yan is an Associate Professor in the Department of Computer Science at Auckland University of Technology, New Zealand. His other publications include the Springer title, Visual Cryptography for Image Processing and Security.
Dr. Yan has contributed to 13 granted research proposals. He has co-authored 13 research books as well as over 230 publications (J: 80+) with more than 2,900 Google citations, one of his research papers has been cited over 700 times. His publications have been accepted or appeared in the ACM and IEEE journals and conferences. Dr. Yan's research distinctions at AUT include deep learning, intelligent surveillance, currency security, visual cryptography, digital event computing, intelligent navigations, etc. Dr. Yan is a regular reviewer of Ph.D. theses of AUT, the Massey University, the University of Canterbury, the University of Auckland (UoA), New Zealand, and the Nanyang Technological University (NTU), Singapore.
Dr. Yan's services have included being a TPC member of all the top ACM and IEEE conferences in his research area, Track Chair of IEEE VCIP 2020 and IEEE ICME 2020, Publication Chair of IAPR ACPR 2019, Program Chair of IEEE AVSS 2018, General Chair of ISGV2021 and IWDW 2013, and Program Chair of WSVS 2015 and IWDCF 2015/2016/2017. Dr. Yan has delivered over 100 talks around the world, and his visit to the Chinese Academy of Sciences China was sponsored by
Dr. Yan is serving as the Editor-in-Chief (EiC) of the International Journal Digital Crime Forensics (IJDCF) from 2014 to 2019, now an Editor-inChief Emeritus; a Guest Editor of the Springer Transactions on Data Hiding and Multimedia Security (DHMS), a book reviewer of John Wiley and Sons, IGI global, and a proposal reviewer of Ministry of Business, Innovation, and Employment (MBIE) of New Zealand. He is also a member of the ACM, the Chair of ACM New Zealand chapter in Multimedia, a senior member of the IEEE, TC members of the IEEE, and a Fellow of the Higher Education Academy (FHEA), UK.
Dr. Wei Qi Yan is an Associate Professor with the Department of Computer Science at Auckland University of Technology, New Zealand. His other publications include the Springer books: Visual Cryptography for Image Processing and Security; Introduction to Intelligent Surveillance.
- Autor: Wei Qi Yan
- 2020, 1st ed. 2021, 134 Seiten, Englisch
- Verlag: Springer International Publishing
- ISBN-10: 3030610810
- ISBN-13: 9783030610814
- Erscheinungsdatum: 04.12.2020
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- Dateiformat: PDF
- Größe: 3.23 MB
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