Keras VS TensorFlow is easily one of the most popular topics among ML enthusiasts. Both of these libraries are prevalent among machine learning and deep learning professionals. Many times, people get confused as to which one they should choose for a particular project.
However, it would be best if you didn’t worry because in this article we’ll find out the difference between Keras and TensorFlow in detail. Let’s dive in:
Keras is a Python-based API for deep neural networks. It simplifies building neural network models and is a high-level API. Keras also supports numerous back-end engines for neural network computations.
The focus of Keras is to follow best practices to reduce cognitive load. With Keras, you can create new models by combining multiple standalone modules such as optimizers, activation functions, neural layers, regularization schemes as well as cost functions.
It runs on top of CNTK, Theano, and TensorFlow, which allows it to offer multiple advantages to developers.
#artificial intelligence #tensorflow