Computational methods for deep learning : theoretic, practice and applications / Wei Qi Yan.
2021
Q325.5
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Details
Title
Computational methods for deep learning : theoretic, practice and applications / Wei Qi Yan.
Author
ISBN
3030610810 (electronic book)
9783030610821 (print)
3030610829
9783030610838 (print)
3030610837
9783030610814 (electronic bk.)
3030610802
9783030610807
9783030610821 (print)
3030610829
9783030610838 (print)
3030610837
9783030610814 (electronic bk.)
3030610802
9783030610807
Published
Cham, Switzerland : Springer, [2021]
Language
English
Description
1 online resource (xvii, 134 pages) : illustrations (some color)
Item Number
10.1007/978-3-030-61081-4 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/1
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file
PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 15, 2021).
Series
Texts in computer science, 1868-0941
Available in Other Form
Print version: 9783030610807
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Table of Contents
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.
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.