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Practical MATLAB deep learning = a p...

Paluszek, Michael.

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Practical MATLAB deep learning = a projects-based approach /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Practical MATLAB deep learning/ by Michael Paluszek, Stephanie Thomas, Eric Ham.
其他題名:	a projects-based approach /
作者:	Paluszek, Michael.
其他作者:	Thomas, Stephanie.
出版者:	Berkeley, CA :Apress : : 2022.,
面頁冊數:	xix, 329 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Machine learning. -
電子資源:	https://doi.org/10.1007/978-1-4842-7912-0
ISBN:	9781484279120

Practical MATLAB deep learning = a projects-based approach /
Paluszek, Michael.

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

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

MATLAB.
Subjects--Topical Terms:

533906
Machine learning.

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

Dewey Class. No.: 006.31

Practical MATLAB deep learning = a projects-based approach /
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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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