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Neural Networks with R

You're reading from   Neural Networks with R Build smart systems by implementing popular deep learning models in R

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781788397872
Length 270 pages
Edition 1st Edition
Languages
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Authors (2):
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Balaji Venkateswaran Balaji Venkateswaran
Author Profile Icon Balaji Venkateswaran
Balaji Venkateswaran
Giuseppe Ciaburro Giuseppe Ciaburro
Author Profile Icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Toc

Table of Contents (8) Chapters Close

Preface 1. Neural Network and Artificial Intelligence Concepts FREE CHAPTER 2. Learning Process in Neural Networks 3. Deep Learning Using Multilayer Neural Networks 4. Perceptron Neural Network Modeling – Basic Models 5. Training and Visualizing a Neural Network in R 6. Recurrent and Convolutional Neural Networks 7. Use Cases of Neural Networks – Advanced Topics

TensorFlow integration with R


TensorFlow is an open source numerical computing library provided by Google for machine intelligence. It hides all of the programming required to build deep learning models and gives the developers a black box interface to program. The Keras API for TensorFlow provides a high-level interface for neural networks.

Python is the de facto programming language for deep learning, but R is catching up. Deep learning libraries are now available with R and a developer can easily download TensorFlow or Keras similar to other R libraries and use them.

In TensorFlow, nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. TensorFlow was originally developed by the Google Brain Team within Google's machine intelligence research for machine learning and deep neural networks research, but it is now available in the public domain. TensorFlow exploits GPU processing when configured...

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