Computational Methods For Deep Learning

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Computational Methods For Deep Learning

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Author : Wei Qi Yan
language : en
Publisher:
Release Date : 2021

Computational Methods For Deep Learning written by Wei Qi Yan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Big data categories.

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.

Computational Methods For Deep Learning

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Author : Wei Qi Yan
language : en
Publisher: Springer Nature
Release Date : 2023-09-15

Computational Methods For Deep Learning written by Wei Qi Yan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-15 with Computers categories.

The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book. The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI). This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.

Computational Mechanics With Neural Networks

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Author : Genki Yagawa
language : en
Publisher: Springer Nature
Release Date : 2021-02-26

Computational Mechanics With Neural Networks written by Genki Yagawa and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-26 with Technology & Engineering categories.

This book shows how neural networks are applied to computational mechanics. Part I presents the fundamentals of neural networks and other machine learning method in computational mechanics. Part II highlights the applications of neural networks to a variety of problems of computational mechanics. The final chapter gives perspectives to the applications of the deep learning to computational mechanics.

Computational Methods And Application In Machine Learning

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Author : Chunwei Tian
language : en
Publisher:
Release Date : 2024-12-18

Computational Methods And Application In Machine Learning written by Chunwei Tian and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-18 with Computers categories.

The present reprint contains 17 in total articles that are accepted and published in the Special Issue "Computational Methods and Application in Machine Learning, 2023" of the MDPI Mathematics journal. The articles cover a wide range of topics with respect to the theory and applications of the computational method in machine learning. These keywords include artificial intelligence big data and analysis, machine learning, deep learning, natural language understanding, pattern recognition, computer vision, information retrieval, data mining, bioinformatics and biomedical applications, reinforcement learning, multimedia analysis and retrieval, multimodal representation learning, feature selection, clustering, etc. Machine learning is an interdisciplinary subject involving probability theory, statistics, approximation theory, convex analysis, optimization, algorithm complexity theory, etc. It focuses on how computers simulate or realize human learning behaviors in order to obtain new knowledge or skills. It is the core of artificial intelligence. In essence, the aim of machine learning is to enable computers to simulate human learning behaviors, automatically acquire knowledge and skills through learning, continuously improve performance, and realize artificial intelligence. We hope the reprint will be interesting and useful for those working in the area of computational methods, machine learning, and artificial intelligence, in addition to those who have a proper mathematical background and are willing to become familiar with recent advances in machine learning, which has entered almost all human life and activity sectors.

Computational Methods In Science And Engineering

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Author : George Maroulis
language : en
Publisher: American Institute of Physics
Release Date : 2009-09-02

Computational Methods In Science And Engineering written by George Maroulis and has been published by American Institute of Physics this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-02 with Computers categories.

The aim of ICCMSE 2008 is to bring together computational scientists and engineers from several disciplines in order to share methods, methodologies and ideas. The potential readers are all the scientists with interest in: Computational Mathematics, Theoretical Physics, Computational Physics, Theoretical Chemistry, Computational Chemistry, Mathematical Chemistry, Computational Engineering, Computational Mechanics, Computational Biology and Medicine, Scientific Computation, High Performance Computing, Parallel and Distributed Computing, Visualization, Problem Solving Environments, Software Tools, Advanced Numerical Algorithms, Modelling and Simulation of Complex Systems, Web-based Simulation and Computing, Grid-based Simulation and Computing, Computational Grids, and Computer Science.

Computational Methods In Drug Design

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Author :
language : en
Publisher:
Release Date : 1993

Computational Methods In Drug Design written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Drugs categories.

Deep Learning In Computational Mechanics

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Author : Leon Herrmann
language : en
Publisher: Springer Nature
Release Date :

Deep Learning In Computational Mechanics written by Leon Herrmann and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.

Computational Methods And Deep Learning For Ophthalmology

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Author : D. Jude Hemanth
language : en
Publisher: Elsevier
Release Date : 2023-02-18

Computational Methods And Deep Learning For Ophthalmology written by D. Jude Hemanth and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-18 with Science categories.

Computational Methods and Deep Learning for Ophthalmology presents readers with the concepts and methods needed to design and use advanced computer-aided diagnosis systems for ophthalmologic abnormalities in the human eye. Chapters cover computational approaches for diagnosis and assessment of a variety of ophthalmologic abnormalities. Computational approaches include topics such as Deep Convolutional Neural Networks, Generative Adversarial Networks, Auto Encoders, Recurrent Neural Networks, and modified/hybrid Artificial Neural Networks. Ophthalmological abnormalities covered include Glaucoma, Diabetic Retinopathy, Macular Degeneration, Retinal Vein Occlusions, eye lesions, cataracts, and optical nerve disorders. This handbook provides biomedical engineers, computer scientists, and multidisciplinary researchers with a significant resource for addressing the increase in the prevalence of diseases such as Diabetic Retinopathy, Glaucoma, and Macular Degeneration. - Presents the latest computational methods for designing and using Decision-Support Systems for ophthalmologic disorders in the human eye - Conveys the role of a variety of computational methods and algorithms for efficient and effective diagnosis of ophthalmologic disorders, including Diabetic Retinopathy, Glaucoma, Macular Degeneration, Retinal Vein Occlusions, eye lesions, cataracts, and optical nerve disorders - Explains how to develop and apply a variety of computational diagnosis systems and technologies, including medical image processing algorithms, bioinspired optimization, Deep Learning, computational intelligence systems, fuzzy-based segmentation methods, transfer learning approaches, and hybrid Artificial Neural Networks

Computational Methods In Biophysics Biomaterials Biotechnology And Medical Systems Algorithm Techniques

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Author : Cornelius T. Leondes
language : en
Publisher:
Release Date : 2003

Computational Methods In Biophysics Biomaterials Biotechnology And Medical Systems Algorithm Techniques written by Cornelius T. Leondes and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Medical categories.

This is the first interdisciplinary reference dedicated to the application of computational methods in biophysics, biomaterials, biotechnology and medical aystems research. (Midwest).

Deep Learning For Hyperspectral Image Analysis And Classification

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Author : Linmi Tao
language : en
Publisher: Springer Nature
Release Date : 2021-02-20

Deep Learning For Hyperspectral Image Analysis And Classification written by Linmi Tao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-20 with Computers categories.

This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly. This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.