Over the past year we’ve been hard at work on creating R interfaces to TensorFlow, an open-source machine learning framework from Google. We are excited about TensorFlow for many reasons, not the least of which is its state-of-the-art infrastructure for deep learning applications.

In the 2 years since it was initially open-sourced by Google, TensorFlow has rapidly become the framework of choice for both machine learning practitioners and researchers. On Saturday, we formally announced our work on TensorFlow during J.J. Allaire’s keynote at rstudio::conf:

In the keynote, J.J. describes not only the work we’ve done on TensorFlow but also discusses deep learning more broadly (what it is, how it works, and where it might be relevant to users of R in the years ahead).

Access to GPUs Training convolutional or recurrent neural networks can be extremely computationally expensive, and benefits significantly from access to a recent high-end NVIDIA GPU. However, most users don’t have this sort of hardware available locally. To address this we have provided a number of ways to use GPUs in the cloud, including: The cloudml package, an R interface to Google’s hosted machine learning engine.

RStudio Server with Tensorflow-GPU for AWS (an Amazon EC2 image preconfigured with NVIDIA CUDA drivers, TensorFlow, the TensorFlow for R interface, as well as RStudio Server).

Detailed instructions for setting up an Ubuntu 16.04 cloud desktop with a GPU using the Paperspace service. There is also documentation on setting up a GPU on your local workstation if you already have the required NVIDIA GPU hardware.

Learning resources We’ve also made a significant investment in learning resources, all of these resources are available on the TensorFlow for R website at https://tensorflow.rstudio.com. Some of the learning resources include: Deep Learning with R

Deep Learning with R is meant for statisticians, analysts, engineers, and students with a reasonable amount of R experience but no significant knowledge of machine learning and deep learning. You’ll learn from more than 30 code examples that include detailed commentary and practical recommendations. You don’t need previous experience with machine learning or deep learning: this book covers from scratch all the necessary basics. You don’t need an advanced mathematics background, either—high school level mathematics should suffice in order to follow along. Deep Learning with Keras Cheatsheet

A quick reference guide to the concepts and available functions in the R interface to Keras. Covers the various types of Keras layers, data preprocessing, training workflow, and pre-trained models. Gallery

In-depth examples of using TensorFlow with R, including detailed explanatory narrative as well as coverage of ancillary tasks like data preprocessing and visualization. A great resource for taking the next step after you’ve learned the basics. Examples

Introductory examples of using TensorFlow with R. These examples cover the basics of training models with the keras, tfestimators, and tensorflow packages.