Keras Introduction for Beginners to Learn Keras
Deep Learning is becoming popular nowadays. It is one of the main fields of Machine Learning. It is becoming more popular in various fields of Data Science like data analysis, image visualization, robotics, AI, image recognition, etc. There are many libraries for Deep Learning like Keras, TensorFlow, Theano. Let us learn Keras Introduction and more.
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Keras is one of the most popular python libraries for Deep Learning. It is the finest choice for Deep Learning models and programs. It is very simple and easy and written in Python. Keras is an API for training different programs. It can run on the top of Theano and TensorFlow. It is a high-level library.
Keras was invented to make the Deep Learning task fast and efficient. It is one of the most useful libraries in Deep learning. It provides an effective and convenient way to learn and train Deep Learning models and programs. Keras can execute on GPU and CPU both. It is compatible with Python 2.7 to 3.6 to date and has cross-platform compatibility.
Keras supports both recurrent networks and convolutional networks. It also enables the combination of both. It enhances the use of multiple platforms. Keras also supports the use of backends. It is because Keras does not handle low-level computations. So, it makes use of other libraries to do this. Keras focuses on the idea of the model. It enables you to develop deep learning projects with fewer lines of code.
Why learn Keras?
It is a very useful library for Deep Learning. It enables the prototyping tasks of Deep Learning easy and fast. Many developers prefer Keras because it helps to optimize their tasks. It saves time and energy. There are many famous organizations that make use of Keras like Google, Netflix, Uber, Microsoft, Huwaei, etc. It has a large adaptation by various industries and research communities. There is around 200,00 user who considers Keras the best platform for Deep Learning. Many of the start-ups working with the model of Machine Learning prefer Keras.
Guiding Principles of Keras
- Modularity: It understands the model as a graph or a sequence. The deep learning model contains discrete components. These components combine in an arbitrary manner.
- User-friendliness: Keras is an easy and user-friendly library. It maximizes readability. Keras provides everything to achieve the outcome without any complexity. It focuses on reducing the load of the users. It also provides clear error messages to the developer.
- Work with Python: All the Deep Learning models are defined in Python code. It makes it easier to work. It also enhances its extensibility feature. There are no separate model files. Everything is in Python.
- Extensibility: it is highly compatible with new components. It is easy to add a new component and work with it. It is simple to add new modules in Keras. Keras allows for creating new modules. It makes Keras more effective.
This was all about Keras Introduction. Keras is a library that provides highly powerful blocks to learn about Deep Learning. It is an open-source, easy, and modular library. It is capable of running on the top of Theano and TensorFlow. Keras optimizes the functions. It saves time making the code execute faster. It performs all low-level computation tasks with the help of other libraries. There is no complexity in using Keras if you know Python. It has a large active community making its development fast and rapid.