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Computational methods for deep learn...

Yan, Wei Qi.

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Computational methods for deep learning = theoretic, practice and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Computational methods for deep learning/ by Wei Qi Yan.
Reminder of title:	theoretic, practice and applications /
Author:	Yan, Wei Qi.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xvii, 134 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	1. Introduction -- 2. Deep Learning Platforms -- 3. CNN and RNN -- 4. Autoencoder and GAN -- 5. Reinforcement Learning -- 6. CapsNet and Manifold Learning -- 7. Boltzmann Machines -- 8. Transfer Learning and Ensemble Learning.
Contained By:	Springer Nature eBook
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-030-61081-4
ISBN:	9783030610814

Computational methods for deep learning = theoretic, practice and applications /
Yan, Wei Qi.

Computational methods for deep learningtheoretic, practice and applications /[electronic resource] :by Wei Qi Yan. - Cham :Springer International Publishing :2021. - xvii, 134 p. :ill. (some col.), digital ;24 cm. - Texts in computer science,1868-0941. - Texts in computer science..

1. Introduction -- 2. Deep Learning Platforms -- 3. CNN and RNN -- 4. Autoencoder and GAN -- 5. Reinforcement Learning -- 6. CapsNet and Manifold Learning -- 7. Boltzmann Machines -- 8. Transfer Learning and Ensemble Learning.

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.

ISBN: 9783030610814

Standard No.: 10.1007/978-3-030-61081-4doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Computational methods for deep learning = theoretic, practice and applications /
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505 0 $a 1. Introduction -- 2. Deep Learning Platforms -- 3. CNN and RNN -- 4. Autoencoder and GAN -- 5. Reinforcement Learning -- 6. CapsNet and Manifold Learning -- 7. Boltzmann Machines -- 8. Transfer Learning and Ensemble Learning.
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