What is the difference between Keras and TensorFlow?

Ans: TensorFlow is an open source platform for machine learning. It’s a comprehensive and flexible environment of tools, libraries and other resources that provide workflows with high-level APIs. The framework offers various levels of concepts for you to choose the one to build and deploy machine learning models.

TensorFlow offers multiple levels of abstraction to build and train models.

TensorFlow lets you train and deploy your model easily, no matter what language or platform you use.

TensorFlow gives you the flexibility and control with features like the Keras Functional API and Model Sub classing API for creation of complex topologies.

Keras, is a high-level neural networks library which is running on the top of TensorFlow, CNTK, and Theano. Using Keras in deep learning allows for easy and fast prototyping as well as running effortlessly on CPU and GPU. This framework can be written in Python code which is easy to debug and allows ease for extensibility.

Keras has a simple, consistent interface optimized for common use cases which provides clear and actionable feedback for user errors.

Keras models are made by connecting configurable building blocks together, with few restrictions.

With the help of Keras, you can easily write custom building blocks for new ideas and researches.

Keras offers consistent & simple APIs which helps in minimizing the number of user actions required for common use cases, also it provides clear and actionable feedback upon user error.