Keras For Deep-Learning
There are several deep learning frameworks available in nowadays. But the problem is why developers commonly used Keras Library?
Before go into that discussion, first we are going to talk about what is Keras? Keras, is a high-level powerful neural network based library that is running on deep learning frameworks.
Why we used Keras for Deep Learning?
Using Keras in deep learning allows for easy and fast prototyping as well as running smoothly on CPU and GPU. This framework is written in Python code which is easy to debug and allows ease for extensibility. Following benefits clearly describe why Keres is special for deep learning.
· User-Friendly: Keras has a simple, consistent interface optimized for common use cases which provides clear and actionable feedback for user errors.
· Modular and Composable: Keras models are made by connecting configurable building blocks together, with few restrictions.
· Easy To Extend: With the help of Keras, you can easily write custom building blocks for new ideas and research.
· Easy To Use: 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.
Keras provides simple and consistent high-level APIs and follows best practices to reduce the cognitive load for the users. In mostly Keras wrap with TensorFlow, But Keras is not depend on TensorFlow. Keras can use TensorFlow as its backend. It can also use other back-ends such as Theano and CNTK.
Then next thing is, How deep learning models are built on Keras?
Deep learning models are built using neural networks. A neural network takes in inputs, which are then processed in hidden layers using weights that are adjusted during training. Then the model spits out a prediction. The weights are adjusted to find patterns in order to make better predictions.
Here I discussed basic idea of the Keras for Deep Learning.
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