Introduction to deep learning using R : a step-by-step guide to learning and implementing deep learning models using R /
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 Lea...
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| Format: | Electronic eBook |
| Language: | English |
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[Berkeley, California?] :
Apress,
[2017]
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| Series: | Books for professionals by professionals.
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| Subjects: | |
| Online Access: | CONNECT |
MARC
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| 100 | 1 | |a Beysolow, Taweh |b II, |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 [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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| 490 | 1 | |a For professionals by professionals | |
| 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. | ||
| 504 | |a Includes bibliographical references and index. | ||
| 590 | |a O'Reilly Online Learning Platform: Academic Edition (SAML SSO Access) | ||
| 650 | 0 | |a Machine learning. | |
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a R (Computer program language) | |
| 730 | 0 | |a WORLDSHARE SUB RECORDS | |
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