Keras is a Python-based high-level neural networks API that is capable of running on top TensorFlow, CNTK, or Theano frameworks used for machine learning. It can be said that Keras acts as the Python Deep Learning Library. Keras was created with emphasis on being user-friendly since the main principle behind it is “designed for human beings, not machines.” The core data structure of Keras is a model, or a way to organize layers.
With the constant technological development we are witnessing in the world of hosting, it is necessary to familiarize ourselves with the terminology and meaning of the systems we are using. An important distinction that we need to make is between virtual machines and containers, as they are both widely used in the hosting industry, and yet they are often confused.
In this tutorial, we are going to set up TensorFlow in a virtual Python environment on Ubuntu 18.04. TensorFlow is an open-source framework, developed by the Google Brain team, designed to be a high-level interface for implementing machine learning and mathematical operations. This library provides developers an avenue to work on complex projects like neural networks through an easy to use Python API. One of the significant benefits of having a Python front-end is that it is portable between operating systems like Linux and Windows.