Machine learning for engineers : using data to solve problems for physical systems / Ryan G. McClarren.
2021
TA347.M33 M33 2021
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Details
Title
Machine learning for engineers : using data to solve problems for physical systems / Ryan G. McClarren.
Author
McClarren, Ryan G., author.
ISBN
9783030703882 (electronic bk.)
3030703886 (electronic bk.)
9783030703875
3030703878
3030703886 (electronic bk.)
9783030703875
3030703878
Published
Cham : Springer, [2021]
Copyright
©2021
Language
English
Description
1 online resource : illustrations (chiefly color)
Other Standard Identifiers
10.1007/978-3-030-70388-2 doi
Call Number
TA347.M33 M33 2021
Dewey Decimal Classification
620.00285/63
Summary
All engineers and applied scientists will need to harness the power of machine learning to solve the highly complex and data intensive problems now emerging. This text teaches state-of-the-art machine learning technologies to students and practicing engineers from the traditionally "analog" disciplines--mechanical, aerospace, chemical, nuclear, and civil. Dr. McClarren examines these technologies from an engineering perspective and illustrates their specific value to engineers by presenting concrete examples based on physical systems. The book proceeds from basic learning models to deep neural networks, gradually increasing readers' ability to apply modern machine learning techniques to their current work and to prepare them for future, as yet unknown, problems. Rather than taking a black box approach, the author teaches a broad range of techniques while conveying the kinds of problems best addressed by each. Examples and case studies in controls, dynamics, heat transfer, and other engineering applications are implemented in Python and the libraries scikit-learn and tensorflow, demonstrating how readers can apply the most up-to-date methods to their own problems. The book equally benefits undergraduate engineering students who wish to acquire the skills required by future employers, and practicing engineers who wish to expand and update their problem-solving toolkit
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 October 8, 2021).
Available in Other Form
Machine learning for engineers.
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Table of Contents
Part I Fundamentals
1. Introduction
2. The landscape of machine learning
3. Linear models
4. Tree-based models
5. Clustering data
Part II Deep Neural Networks
6. Feed-forward Neural networks
7.convolutional neural networks
8. Recurrent neural networks for time series data
Part III Advanced topics in machine learning
9. Unsupervised learning with neural networks
10. Reinforcement learning
11. Transfer learning
Part IV Appendixes
Appendix A. Sci-Kit learn
Appendix B. Tensorflow.
1. Introduction
2. The landscape of machine learning
3. Linear models
4. Tree-based models
5. Clustering data
Part II Deep Neural Networks
6. Feed-forward Neural networks
7.convolutional neural networks
8. Recurrent neural networks for time series data
Part III Advanced topics in machine learning
9. Unsupervised learning with neural networks
10. Reinforcement learning
11. Transfer learning
Part IV Appendixes
Appendix A. Sci-Kit learn
Appendix B. Tensorflow.