| Description |
1 online resource |
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text file PDF rda |
| Series |
For professionals by professionals |
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Books for professionals by professionals.
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| Contents |
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. |
| Summary |
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. |
| Bibliography |
Includes bibliographical references and index. |
| Subject |
Machine learning.
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Big data.
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R (Computer program language)
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Apprentissage automatique. |
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Données volumineuses. |
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R (Langage de programmation) |
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Artificial intelligence. |
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Programming & scripting languages: general. |
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Business mathematics & systems. |
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Big data |
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Machine learning |
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R (Computer program language) |
| Other Form: |
Print version: Beysolow, Taweh, II. Introduction to deep learning using R. [Berkeley, California?] : Apress, [2017] 9781484227336 1484227336 (OCoLC)973920041 |
| ISBN |
9781484227336 (paperback) |
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1484227336 (paperback) |
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9781484227343 |
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1484227344 |
| Standard No. |
10.1007/978-1-4842-2734-3 doi |
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