Computational methods for deep learning : theory, algorithms, and implementations / Wei Qi Yan.
2023
Q325.5
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Citation
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Linked Resource
Concurrent users
Unlimited
Authorized users
Authorized users
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Can lend chapters, not whole ebooks
Details
Title
Computational methods for deep learning : theory, algorithms, and implementations / Wei Qi Yan.
Author
Edition
Second edition.
ISBN
9789819948239 (electronic bk.)
9819948231 (electronic bk.)
9789819948222
9819948223
9819948231 (electronic bk.)
9789819948222
9819948223
Published
Singapore : Springer, 2023.
Language
English
Description
1 online resource (xx, 222 pages) : illustrations (some color).
Item Number
10.1007/978-981-99-4823-9 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/1
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed September 20, 2023).
Series
Texts in computer science.
Available in Other Form
Print version: 9789819948222
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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.