東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Practical MATLAB deep learning = a p...

Paluszek, Michael.

Linked to FindBook

Google Book

Amazon

博客來

Practical MATLAB deep learning = a projects-based approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Practical MATLAB deep learning/ by Michael Paluszek, Stephanie Thomas, Eric Ham.
Reminder of title:	a projects-based approach /
Author:	Paluszek, Michael.
other author:	Thomas, Stephanie.
Published:	Berkeley, CA :Apress : : 2022.,
Description:	xix, 329 p. :ill., digital ;24 cm.
[NT 15003449]:	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
Subject:	Machine learning. -
Online resource:	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 /
LDR:02650nmm a2200337 a 4500 001 2304436
003 DE-He213
005 20220910175706.0
006 m d
007 cr nn 008maaau
008 230409s2022 cau s 0 eng d
020 $a 9781484279120 $q (electronic bk.)
020 $a 9781484279113 $q (paper)
024 7 $a 10.1007/978-1-4842-7912-0 $2 doi
035 $a 978-1-4842-7912-0
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5 $b .P35 2022
072 7 $a UMX $2 bicssc
072 7 $a COM000000 $2 bisacsh
072 7 $a UMX $2 thema
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .P184 2022
100 1 $a Paluszek, Michael. $3 2163258
245 1 0 $a Practical MATLAB deep learning $h [electronic resource] : $b a projects-based approach / $c by Michael Paluszek, Stephanie Thomas, Eric Ham.
250 $a Second edition.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2022.
300 $a xix, 329 p. : $b ill., digital ; $c 24 cm.
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.
630 0 0 $a MATLAB. $3 532595
650 0 $a Machine learning. $3 533906
650 1 4 $a Programming Language. $3 3538935
650 2 4 $a Compilers and Interpreters. $3 3592044
650 2 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Maker. $3 3538489
650 2 4 $a Mathematics of Computing. $3 891213
700 1 $a Thomas, Stephanie. $3 2163259
700 1 $a Ham, Eric. $3 3606656
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-7912-0
950 $a Professional and Applied Computing (SpringerNature-12059)