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Hands-On Deep Learning with R: A practical guide to designing, building, and improving neural network models using R - Softcover
Synopsis
Explore and implement deep learning to solve various real-world problems using modern R libraries such as TensorFlow, MXNet, H2O, and Deepnet
Key Features
- Understand deep learning algorithms and architectures using R and determine which algorithm is best suited for a specific problem
- Improve models using parameter tuning, feature engineering, and ensembling
- Apply advanced neural network models such as deep autoencoders and generative adversarial networks (GANs) across different domains
About the Author
Michael Pawlus is a data scientist at The Ohio State University where he is currently part of the team building of the data science infrastructure for the Advancement department while also leading the implementation of innovative projects there. Prior to this, Michael was a data scientist at the University of Southern California. In addition to this work, Michael has chaired data science education conferences, published articles on...
"About this title" may belong to another edition of this title.
- PublisherPackt Publishing
- Publication date2020
- ISBN 10 1788996836
- ISBN 13 9781788996839
- BindingPaperback
- LanguageEnglish
- Number of pages330
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