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Introduction to deep learning using ...

Beysolow, Taweh.

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Introduction to deep learning using R = a step-by-step guide to learning and implementing deep learning models using R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Introduction to deep learning using R/ by Taweh Beysolow II.
Reminder of title:	a step-by-step guide to learning and implementing deep learning models using R /
Author:	Beysolow, Taweh.
Published:	Berkeley, CA :Apress : : 2017.,
Description:	xix, 227 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: What is Deep Learning? -- Chapter 2: Mathematical Review -- Chapter 3: A Review of Optimization and Machine Learning -- Chapter 4: Single and Multi-Layer Perceptron Models -- Chapter 5: Convolutional Neural Networks (CNNs) -- Chapter 6: Recurrent Neural Networks (RNNs) -- Chapter 7: Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks -- Chapter 8: Experimental Design and Heuristics -- Chapter 9: Deep Learning and Machine Learning Hardware/Software Suggestions -- Chapter 10: Machine Learning Example Problems -- Chapter 11: Deep Learning and Other Example Problems -- Chapter 12: Closing Statements.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	http://dx.doi.org/10.1007/978-1-4842-2734-3
ISBN:	9781484227343

Introduction to deep learning using R = a step-by-step guide to learning and implementing deep learning models using R /
Beysolow, Taweh.

Introduction to deep learning using Ra step-by-step guide to learning and implementing deep learning models using R /[electronic resource] :by Taweh Beysolow II. - Berkeley, CA :Apress :2017. - xix, 227 p. :ill., digital ;24 cm.

Chapter 1: What is Deep Learning? -- Chapter 2: Mathematical Review -- Chapter 3: A Review of Optimization and Machine Learning -- Chapter 4: Single and Multi-Layer Perceptron Models -- Chapter 5: Convolutional Neural Networks (CNNs) -- Chapter 6: Recurrent Neural Networks (RNNs) -- Chapter 7: Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks -- Chapter 8: Experimental Design and Heuristics -- Chapter 9: Deep Learning and Machine Learning Hardware/Software Suggestions -- Chapter 10: Machine Learning Example Problems -- Chapter 11: Deep Learning and Other Example Problems -- Chapter 12: Closing Statements.

ISBN: 9781484227343

Standard No.: 10.1007/978-1-4842-2734-3doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5 / .B49 2017

Dewey Class. No.: 006.31

Introduction to deep learning using R = a step-by-step guide to learning and implementing deep learning models using R /
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245 1 0 $a Introduction to deep learning using R $h [electronic resource] : $b a step-by-step guide to learning and implementing deep learning models using R / $c by Taweh Beysolow II.
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300 $a xix, 227 p. : $b ill., digital ; $c 24 cm.
505 0 $a Chapter 1: What is Deep Learning? -- Chapter 2: Mathematical Review -- Chapter 3: A Review of Optimization and Machine Learning -- Chapter 4: Single and Multi-Layer Perceptron Models -- Chapter 5: Convolutional Neural Networks (CNNs) -- Chapter 6: Recurrent Neural Networks (RNNs) -- Chapter 7: Autoencoders, Restricted Boltzmann Machines, and Deep Belief Networks -- Chapter 8: Experimental Design and Heuristics -- Chapter 9: Deep Learning and Machine Learning Hardware/Software Suggestions -- Chapter 10: Machine Learning Example Problems -- Chapter 11: Deep Learning and Other Example Problems -- Chapter 12: Closing Statements.
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650 0 $a R (Computer program language) $3 784593
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650 2 4 $a Computing Methodologies. $3 830243
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