Keras is another machine learning library designed for the Python language. In this article I will try to give you a little introduction to the capabilities of this library.
What is Keras?
As I mentioned in the introduction, Keras is a machine learning library for the Python language. It was first released in 2015, and the library's creator is French programmer François Chollet. Keras applies best practices for reducing cognitive load: it offers consistent and simple APIs, minimizes the number of user actions required for typical use cases, and provides clear and useful error messages. It also includes extensive documentation and guides for developers. It is worth mentioning that Keras allows switching between different interfaces. The frameworks supported by Keras are: Tensorflow, Theano, PlaidML, MXNet and CNTK.
Keras is designed to be easy to learn and simple to use. It offers consistent and simple APIs, clearly explains user errors. Prototyping time in Keras is short, which translates into fast deployment. Keras is also deeply integrated with TensorFlow. It also runs smoothly on both CPU and GPU. It handles almost all neural networks. In addition, Keras has a very extensive community, so finding help with a problem shouldn't be difficult. The documentation is extensive, as well as easy to read. Keras is used commercially by companies such as Netflix, Uber, Square, Yelp, etc.