Introduction to deep learning using R : a step-by-step guide to learning and implementing deep learning models using R
(eBook)
Author
Published
[Berkeley, California?] : Apress, [2017].
Format
eBook
ISBN
1484227336, 1484227344, 9781484227336, 9781484227343
Physical Desc
1 online resource
Status
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Language
English
UPC
10.1007/978-1-4842-2734-3
Notes
Bibliography
Includes bibliographical references and index.
Description
Understand deep learning, the nuances of its different models, and where these models can be applied. 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. What You Will Learn: • Understand the intuition and mathematics that power deep learning models • Utilize various algorithms using the R programming language and its packages • Use best practices for experimental design and variable selection • Practice the methodology to approach and effectively solve problems as a data scientist • Evaluate the effectiveness of algorithmic solutions and enhance their predictive power.
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Citations
APA Citation, 7th Edition (style guide)
Beysolow, T. (2017). Introduction to deep learning using R: a step-by-step guide to learning and implementing deep learning models using R . Apress.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Beysolow, Taweh. 2017. Introduction to Deep Learning Using R: A Step-by-step Guide to Learning and Implementing Deep Learning Models Using R. Apress.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Beysolow, Taweh. Introduction to Deep Learning Using R: A Step-by-step Guide to Learning and Implementing Deep Learning Models Using R Apress, 2017.
MLA Citation, 9th Edition (style guide)Beysolow, Taweh. Introduction to Deep Learning Using R: A Step-by-step Guide to Learning and Implementing Deep Learning Models Using R Apress, 2017.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
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Grouped Work ID
1711d0d8-bfb2-3a03-4336-c8569cdf2a3d-eng
Grouping Information
| Grouped Work ID | 1711d0d8-bfb2-3a03-4336-c8569cdf2a3d-eng |
|---|---|
| Full title | introduction to deep learning using r a step by step guide to learning and implementing deep learning models using r |
| Author | beysolow taweh |
| Grouping Category | book |
| Last Update | 2024-09-06 16:31:08PM |
| Last Indexed | 2024-09-18 04:08:12AM |
Book Cover Information
| Image Source | syndetics |
|---|---|
| First Loaded | Sep 4, 2024 |
| Last Used | Sep 8, 2024 |
Marc Record
| First Detected | Jul 29, 2024 04:03:10 PM |
|---|---|
| Last File Modification Time | Sep 06, 2024 04:38:04 PM |
MARC Record
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| 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 [Berkeley, California?] :|b Apress,|c [2017] | |
| 264 | 2 | |a New York, NY :|b Distributed by Springer Science + Business Media | |
| 264 | 4 | |c ©2017 | |
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| 504 | |a Includes bibliographical references and index. | ||
| 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. | |
| 520 | |a Understand deep learning, the nuances of its different models, and where these models can be applied. 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. What You Will Learn: • Understand the intuition and mathematics that power deep learning models • Utilize various algorithms using the R programming language and its packages • Use best practices for experimental design and variable selection • Practice the methodology to approach and effectively solve problems as a data scientist • Evaluate the effectiveness of algorithmic solutions and enhance their predictive power. | ||
| 650 | 0 | |a Machine learning.|0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Big data.|0 http://id.loc.gov/authorities/subjects/sh2012003227 | |
| 650 | 0 | |a R (Computer program language)|0 http://id.loc.gov/authorities/subjects/sh2002004407 | |
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| 776 | 0 | 8 | |i Print version:|a Beysolow, Taweh, II.|t Introduction to deep learning using R.|d [Berkeley, California?] : Apress, [2017]|z 9781484227336|z 1484227336|w (OCoLC)973920041 |
| 830 | 0 | |a Books for professionals by professionals. | |
| 856 | 4 | 0 | |u https://www.aclib.us/OReilly |