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Practical MATLAB deep learninga proj...

Paluszek, Michael.

Practical MATLAB deep learninga project-based approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Practical MATLAB deep learningby Michael Paluszek, Stephanie Thomas.
Reminder of title:	a project-based approach /
Author:	Paluszek, Michael.
other author:	Thomas, Stephanie.
Published:	Berkeley, CA :Apress :2020.
Description:	xv, 252 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-1-4842-5124-9
ISBN:	9781484251249$q(electronic bk.)

Practical MATLAB deep learninga project-based approach /
Paluszek, Michael.

Practical MATLAB deep learninga project-based approach /[electronic resource] :by Michael Paluszek, Stephanie Thomas. - Berkeley, CA :Apress :2020. - xv, 252 p. :ill., digital ;24 cm.

1 What is Deep Learning? -- 2 MATLAB Machine and Deep Learning Toolboxes -- 3 Finding Circles with Deep Learning -- 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.

Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. You'll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. Along the way, you'll learn to model complex systems, including the stock market, natural language, and angles-only orbit determination. You'll cover dynamics and control, and integrate deep-learning algorithms and approaches using MATLAB. You'll also apply deep learning to aircraft navigation using images. Finally, you'll carry out classification of ballet pirouettes using an inertial measurement unit to experiment with MATLAB's hardware capabilities. 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: 9781484251249$q(electronic bk.)

Standard No.: 10.1007/978-1-4842-5124-9doiSubjects--Uniform Titles:

MATLAB.
Subjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .P35 2020

Dewey Class. No.: 006.31

Practical MATLAB deep learninga project-based approach /
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505 0 $a 1 What is Deep Learning? -- 2 MATLAB Machine and Deep Learning Toolboxes -- 3 Finding Circles with Deep Learning -- 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.
520 $a Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. You'll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. Along the way, you'll learn to model complex systems, including the stock market, natural language, and angles-only orbit determination. You'll cover dynamics and control, and integrate deep-learning algorithms and approaches using MATLAB. You'll also apply deep learning to aircraft navigation using images. Finally, you'll carry out classification of ballet pirouettes using an inertial measurement unit to experiment with MATLAB's hardware capabilities. 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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