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

The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these task...

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Bibliographic Details
Main Author: Beysolow II, Taweh, (Author)
Format: eBook
Language: English
Published: [Place of publication not identified] : Apress®, [2017]
Subjects:
Machine learning.
R (Computer program language)
Big data.
Programming languages (Electronic computers)
elektronické knihy
electronic books
ISBN: 9781484227343
9781484227336
Physical Description: 1 online resource (xix, 227 pages)

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100 1 |a Beysolow II, Taweh,  |e author. 
245 1 0 |a Introduction to deep learning using R :  |b a step-by-step guide to learning and implementing deep learning models using R /  |c Taweh Beysolow II. 
264 1 |a [Place of publication not identified] :  |b Apress®,  |c [2017] 
264 4 |c ©2017 
300 |a 1 online resource (xix, 227 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a počítač  |b c  |2 rdamedia 
338 |a online zdroj  |b cr  |2 rdacarrier 
505 0 |a Introduction to deep learning -- Mathematical review -- A review of optimization and machine learning -- Single and multilayer perceptron models -- Convolutional neural networks (CNNs) -- Recurrent neural networks (RNNs) -- Autoencoders, restricted boltzmann machines, and deep belief networks -- Experimental design and heuristics -- Hardware and software suggestions -- Machine learning example problems -- Deep learning and other example problems -- Closing statements. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a The abundance of data and demand for superior products/services have driven the development of advanced computer science techniques, among them image and speech recognition. Introduction to Deep Learning Using R provides a theoretical and practical understanding of the models that perform these tasks by building upon the fundamentals of data science through machine learning and deep learning. This step-by-step guide will help you understand the disciplines so that you can apply the methodology in a variety of contexts. All examples are taught in the R statistical language, allowing students and professionals to implement these techniques using open source tools. 
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650 0 |a Machine learning. 
650 0 |a R (Computer program language) 
650 0 |a Big data. 
650 0 |a Programming languages (Electronic computers) 
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