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Practical MATLAB deep learninga projects-based approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Practical MATLAB deep learningby Michael Paluszek, Stephanie Thomas, Eric Ham.
Reminder of title:	a projects-based approach /
other author:	Thomas, Stephanie.
Published:	Berkeley, CA :Apress :2022.
Description:	xix, 329 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-1-4842-7912-0
ISBN:	9781484279120$q(electronic bk.)

Practical MATLAB deep learninga projects-based approach /
Practical MATLAB deep learninga projects-based approach /[electronic resource] :by Michael Paluszek, Stephanie Thomas, Eric Ham. - Second edition. - Berkeley, CA :Apress :2022. - xix, 329 p. :ill., digital ;24 cm.

1. What is deep learning? -- 2. MATLAB Toolboxes -- 3. Finding Circles -- 4. Classifying Movies -- 5. Algorithmic Deep Learning -- 6. Tokamak Disruption Detection -- 7. Classifying a Pirouette -- 8. Completing Sentences -- 9. Terrain Based Navigation -- 10. Stock Prediction -- 11. Image Classification -- 12. Orbit Determination -- 13. Earth Sensors -- 14. Generative Modeling of Music -- 15. Reinforcement Learning -- Bibliography.

Harness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. In this book, you'll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. This edition includes new and expanded projects, and covers generative deep learning and reinforcement learning. Over the course of the book, you'll learn to model complex systems and apply deep learning to problems in those areas. Applications include: Aircraft navigation An aircraft that lands on Titan, the moon of Saturn, using reinforcement learning Stock market prediction Natural language processing Music creation usng generative deep learning Plasma control Earth sensor processing for spacecraft MATLAB Bluetooth data acquisition applied to dance physics You will: Explore deep learning using MATLAB and compare it to algorithms Write a deep learning function in MATLAB and train it with examples Use MATLAB toolboxes related to deep learning Implement tokamak disruption prediction.

ISBN: 9781484279120$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-7912-0doiSubjects--Uniform Titles:

MATLAB.
Subjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .P35 2022

Dewey Class. No.: 006.31

Practical MATLAB deep learninga projects-based approach /
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505 0 $a 1. What is deep learning? -- 2. MATLAB Toolboxes -- 3. Finding Circles -- 4. Classifying Movies -- 5. Algorithmic Deep Learning -- 6. Tokamak Disruption Detection -- 7. Classifying a Pirouette -- 8. Completing Sentences -- 9. Terrain Based Navigation -- 10. Stock Prediction -- 11. Image Classification -- 12. Orbit Determination -- 13. Earth Sensors -- 14. Generative Modeling of Music -- 15. Reinforcement Learning -- Bibliography.
520 $a Harness the power of MATLAB for deep-learning challenges. Practical MATLAB Deep Learning, Second Edition, remains a one-of a-kind book that provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. In this book, you'll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. This edition includes new and expanded projects, and covers generative deep learning and reinforcement learning. Over the course of the book, you'll learn to model complex systems and apply deep learning to problems in those areas. Applications include: Aircraft navigation An aircraft that lands on Titan, the moon of Saturn, using reinforcement learning Stock market prediction Natural language processing Music creation usng generative deep learning Plasma control Earth sensor processing for spacecraft MATLAB Bluetooth data acquisition applied to dance physics You will: Explore deep learning using MATLAB and compare it to algorithms Write a deep learning function in MATLAB and train it with examples Use MATLAB toolboxes related to deep learning Implement tokamak disruption prediction.
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