TensorFlow is an open source numerical computing library provided by Google for machine intelligence. It hides all of the programming required to build deep learning models and gives the developers a black box interface to program. The Keras API for TensorFlow provides a high-level interface for neural networks.

Python is the de facto programming language for deep learning, but R is catching up. Deep learning libraries are now available with R and a developer can easily download TensorFlow or Keras similar to other R libraries and use them.

In TensorFlow, nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. TensorFlow was originally developed by the Google Brain Team within Google's machine intelligence research for machine learning and deep neural networks research, but it is now available in the public domain. TensorFlow exploits GPU processing when configured appropriately.

The generic use cases for TensorFlow are as follows:

• Image recognition
• Computer vision
• Voice/sound recognition
• Time series analysis
• Language detection
• Language translation
• Text-based processing
• Handwriting Recognition (HWR)
• Many others

In this section, we will see how we can bring TensorFlow libraries into R. This will open up a huge number of possibilities with deep learning using TensorFlow in R. In order to use TensorFlow, we must first install Python. If you don't have a Python installation on your machine, it's time to get it.

Python is a dynamic Object-Oriented Programming (OOP) language that can be used for many types of software development. It offers strong support for integration with other languages and programs, is provided with a large standard library, and can be learned within a few days. Many Python programmers can confirm a substantial increase in productivity and feel that it encourages the development of higher quality code and maintainability. Python runs on Windows, Linux/Unix, macOS X, OS/2, Amiga, Palm Handhelds, and Nokia phones. It also works on Java and .NET virtual machines. Python is licensed under the OSI-approved open source license; its use is free, including for commercial products.

Python was created in the early 1990s by Guido van Rossum at Stichting Mathematisch Centrum in the Netherlands as a successor of a language called ABC. Guido remains Python's principal author, although it includes many contributions from others.

After properly installing the Python version of our machine, we have to worry about installing TensorFlow. We can retrieve all library information and available versions of the operating system from the following link: https://www.tensorflow.org/.

Also, in the install section, we can find a series of guides that explain how to install a version of TensorFlow that allows us to write applications in Python. Guides are available for the following operating systems:

• Installing TensorFlow on Ubuntu
• Installing TensorFlow on macOS X
• Installing TensorFlow on Windows
• Installing TensorFlow from sources

For example, to install Tensorflow on Windows, we must choose one of the following types:

• TensorFlow with CPU support only
• TensorFlow with GPU support

To install TensorFlow, start a terminal with privileges as administrator. Then issue the appropriate pip3 install command in that terminal. To install the CPU-only version, enter the following command:

C:> pip3 install --upgrade tensorflow

A series of code lines will be displayed on the video to keep us informed of the execution of the installation procedure, as shown in the following figure:

At this point, we can return to our favorite environment; I am referring to the R development environment. We will need to install the interface to TensorFlow. The R interface to TensorFlow lets you work productively using the high-level Keras and Estimator APIs, and when you need more control, it provides full access to the core TensorFlow API. To install the R interface to TensorFlow, we will use the following procedure.

First, install the tensorflow R package from CRAN as follows:

install.packages("tensorflow")

Then, use the install_tensorflow() function to install TensorFlow (for a proper installation procedure, you must have administrator privileges):

library(tensorflow)
install_tensorflow()

We can confirm that the installation succeeded:

sess = tf$Session()
hello <- tf$constant('Hello, TensorFlow!')
sess$run(hello)

This will provide you with a default installation of TensorFlow suitable for use with the tensorflow R package. Read on if you want to learn about additional installation options, including installing a version of TensorFlow that takes advantage of NVIDIA GPUs if you have the correct CUDA libraries installed. In the following code, we can check the success of the installation:

> library(tensorflow)
> sess = tf$Session()
> hello <- tf$constant('Hello, TensorFlow!')
> sess$run(hello)
b'Hello, TensorFlow!'