The R interface to TensorFlow consists of a suite of R packages that provide a variety of interfaces to TensorFlow for different tasks and levels of abstraction, including: keras—A high-level interface for neural networks, with a focus on enabling fast experimentation.Click to see full answer. Likewise, is R good for deep learning? When to Use R is also an excellent choice for projects that require a one-time dive into a dataset. R is an excellent choice if data analytics or visualization is at the core of your project. It enables rapid prototyping and working with datasets to develop Machine Learning models.Subsequently, question is, should I use keras or TensorFlow? Tensorflow is the most famous library used in production for deep learning models. However TensorFlow is not that easy to use. On the other hand, Keras is a high level API built on TensorFlow (and can be used on top of Theano too). It is more user-friendly and easy to use as compared to TF. Beside above, should I learn R or Python? In a nutshell, he says, Python is better for for data manipulation and repeated tasks, while R is good for ad hoc analysis and exploring datasets. R has a steep learning curve, and people without programming experience may find it overwhelming. Python is generally considered easier to pick up.How do you implement R? Run R Programming in Windows Go to official site of R programming. Click on the CRAN link on the left sidebar. Select a mirror. Click “Download R for Windows” Click on the link that downloads the base distribution. Run the file and follow the steps in the instructions to install R.