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R Deep Learning Projects

You're reading from   R Deep Learning Projects Master the techniques to design and develop neural network models in R

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788478403
Length 258 pages
Edition 1st Edition
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Authors (2):
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Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Author Profile Icon Yuxi (Hayden) Liu
Yuxi (Hayden) Liu
Pablo Maldonado Pablo Maldonado
Author Profile Icon Pablo Maldonado
Pablo Maldonado
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Getting ready

In this chapter, we will introduce keras and tensorflow for R. keras is a model-level building, in that it provides a high-level interface to quickly develop deep learning models. Instead of implementing low-level operations such as convolutions and tensor products, it relies on Theano, TensorFlow or CNTK in the backend, and according to the development team, more backends would be supported in the future. 

Why do you need a backend? Well, if the computation becomes more complicated, which is often the case in deep learning, you need to use different computation methods (known as computation graphs) and hardware (GPUs). For instructional purposes, all our sample codes run without GPU. 

Installing Keras and TensorFlow for R

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