R for DNNs

In the previous section, we clarified some key concepts that are at the deep learning base. We also understood the features that make the use of deep learning particularly convenient. Moreover, its rapid diffusion is also due to the great availability of a wide range of frameworks and libraries for various programming languages.

The R programming language is widely used by scientists and programmers, thanks to its extreme ease of use. Additionally, there is an extensive collection of libraries that allow professional data visualization and analysis with the most popular algorithms. The rapid diffusion of deep learning algorithms has led to the creation of an ever-increasing number of packages available for deep learning, even in R.

The following table shows the various packages/interfaces available for deep learning using R:

CRAN package	Supported taxonomy of neural network	Underlying language/vendor
MXNet	Feed-forward, CNN	C/C++/CUDA
darch	RBM, DBN	C/C++
deepnet	Feed-forward, RBM, DBN, autoencoders...