Keras is an open-source library that provides an interface for Python for artificial neural networks. It provides an interface for the TensorFlow library. It enables faster experimentation with deep neural networks. Keras supported multiple backends such as TensorFlow, Microsoft Cognitive Toolkit, Theano and PlaidML  until version 2.3. 2.4 version supports only TensorFlow. Its primary focus is on being user-friendly, modular and extensible tools besides being designed to enable faster experimentation with deep neural networks. Keras was developed as a part of project open-ended Neuro Electronic Intelligent Robot Operating System (ONEIROS).

Keras, like any other system, possesses a few features that makes it easier and more comfortable to work with. 

• It has many implementations of commonly used neural network building elements such as layers, objectives, activation functionalities, optimizers.
• It implements a host of tools that makes working with image and text data easier to simplify the programming needed for developing deep neural code. 
• The programming codes are hosted on GitHub.
• It is a community that supports forums which includes the GitHub issues page and slack channel. 
• It supports standard neural networks.
• It supports convolution neural networks and recurrent neural networks. 
• It supports utility layers like dropout, batch normalization and pooling. 
• It allows the users to produce the deep model on smartphones like mobiles, tablets, etc in both Android and iOS devices. . 
• It produces deep models on the web and also on the Java Virtual Machine or JVM. 
• It allows the use of distributed training for deep learning models on the clusters of GPU or Graphics Processing Units and TPU or Tensor Processing Units. 

Keras combines the efficient numerical computation libraries such as Theano and TensorFlow. Ot is a tool that helps users to define and train deep learning neural network models in a simple and fewer lines of code. 

Keras sees its its uses in many fields like in:

• Deep learning. Keras API is designed for human use, to make the working faster and much more accurate. It follows certain practices to reduce the cognitive load. It provides consistent and simple APLs and also minimizes the number of user involvement needed for common use cases. It renders clear and actionable messages. It has extensive documentation and developer guides. 
• It is fast. Keras is the framework deep learning technique that is most used on Kaggle. Since it makes it simpler and easier to run new experiments, keras empowers users to innovate unique ideas in a very fast approach. This is the success story of Keras. 
• It is an exascale machine learning tool. Keras is developed on TensorFlow 2. It is a powerful framework for industry that can scale to vast clusters of GPU or the TPU pod. It is easier and possible to work with. 
• Keras framework applications can be deployed anywhere. It has good deployment capabilities of the TensorFlow platform. The models can be exported to JavaScript and can be run directly on the browser, TF Lite to run on iOS, Android and other embedded devices. It is easier to serve the models through web APIs. 
• It serves as a vast ecosystem. It serves as a central part of the tightly packed TensorFlow2 ecosystem, covering every step of the machine learning process flow. It serves data management to hyperparameter training to deployment solutions. 
• It is a part of state-of-art research. It is used by CERN, NASA, NIH, LCH and many other top scientific research organizations around the globe. It possesses low level flexibility to implant in arbitrary research thoughts, however, it offers optional high level features of convenience to speed up the experimentation cycles. 
• It is easily accessible. Since Keras is an easy to use platform and prioritizes user experience, it makes the deep learning solution of choice for many globally accepted courses. It is widely used and highly recommended because it is one of the best approaches to understand deep learning. 

The free Keras certification course offered by Great Learning helps you to understand the framework better. This Keras course shall give you a better hold of the applications, working and different features it provides to its users for better experience and development of the applications. At the end of the session, you will be able to fully work with the Keras platform. You can also learn Keras for free whenever you want. You will earn a certificate after the successful completion of the Keras course. Happy learning!