TensorFlow, a general purpose numerical computing library, was nominally developed for python and has been proving support for approximately 2 years now. This is one of the reasons why Python has always been preferred over R.
Rstudio (a free and open-source integrated development environment ) made R Interface with TensorFlow plausible. Rstudio formally announced their work on creating R interfaces to TensorFlow at rstudio::conf on Saturday. Here is JJ Allaire, the CEO of Rstudio, addressing the conference.
Interfacing R and TensorFlow has a suite of packages that provides high-level interfaces to deep learning models (Keras) and standard regression and classification models (Estimators). Here we have some interfaces to TensorFlow:
Considering the fact that not all users will have complete access to high-end NVIDIA GPU, using GPUs in the cloud has been made possible. Here are a few methods for the same :
On the other hand, for a user having required NVIDIA GPU hardware, here are steps to set up GPU in the local workstation.
To make this simpler for the users, Rstudio has provided all the resources on TensorFlow for R website. You can also refer Deep Learning using Keras and TensorFlow in R.
It has always been a major topic of discussion to choose between R and Python. Python was given the preference as it could be interfaced with TensorFlow and Keras. The creation of R interface with TensorFlow is a good news for all R users.
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