This makes TensorFlow an excellent choice for training distributed deep learning networks in an architecture agnostic way. TensorFlow is extremely flexible, allowing you to deploy network computation to multiple CPUs, GPUs, servers, or even mobile systems without having to change a single line of code. Keras abstracts away much of the complexity of building a deep neural network, leaving us with a very simple, nice, and easy to use interface to rapidly build, test, and deploy deep learning architectures.

Keras is simply a wrapper around more complex numerical computation engines such as TensorFlow and Theano. Keras has easy syntax and can use Google TensorFlow or Microsoft CNTK or Theano as its backend. Keras is a great choice to learn machine learning and deep learning. We gratefully acknowledge the support of NVIDIA Corporation with awarding one Titan X Pascal GPU used for our machine learning and deep learning based research. This post introduces how to install Keras with TensorFlow as backend on Ubuntu Server 16.04 LTS with CUDA 8 and a NVIDIA TITAN X (Pascal) GPU, but it should work for Ubuntu Desktop 16.04 LTS.

Install cuda on ubuntu 16.04