Deep learning is designed and built to imitate the neural networks of the human brain and the way they function. These neural networks are the pathways used by the brain in processing the information it receives to recognize voices and sounds, translate words and phrases, and to make decisions. In recent months, a deep-learning interface which has enjoyed an uptick in popularity is Keras.
What Is Keras?
Keras is an Application Programming Interface (API) written in Python code that you can use to build functions. It’s an open-source neural network library designed to support fast experimentation using deep neural networks. It’s actually built on top of TensorFlow, which can be employed as its backend. Aside from TensorFlow, Keras can also run on top of other deep-learning frameworks.
If you want to know more about the reasons behind the rise of Keras as the preferred deep-learning interface of choice, you might want to read on.
- Open-Source And User-Friendly, Minimum Programming Needed
One of the main reasons behind the rising popularity of Keras is its open-source and user-friendly nature. It’s basically a user-friendly deep-learning library designed and created by Francois Chollet, a former deep-learning researcher at Google. It’s one of the recommended skills to acquire in 2021.
With its user-friendly functionalities, newbies in deep learning are able to turn their ideas into simple codes, and those simple codes can be quickly turned into marketable products. This makes it very popular in the academe and almost all industries.
Keras was designed by its creators to be user friendly because they want to make machine learning available to everyone who wants to try. They made it in such a way that its features were designed and built so that they can be used by someone who doesn’t have advanced computer programming skills.
- Simplified And Designed For Developer
The second reason behind the rising popularity of Keras among new and expert artificial intelligence (AI) professionals is its very simple, easy-to-use modules. It has modules for almost every aspect of the neural network model-design-and-building process.
With the help of these easy-to-use functions, AI developers are able to do their work more efficiently. They can now do rapid model training and robust model testing. It has also enabled them to conduct fast experiments and flexible deployments.
- Widely Used In Research And Industry
Another reason behind the popularity of Keras is that it’s widely used in industry, academe, and research institutions. Industry professionals as well as academic experts have found that its easy-to-use functionalities make it possible for them to conduct more efficient research and come up with a prototype within a shorter time frame.
One example is that experts who have very little or no programming skills and deep learning know-how have realized that they can use Keras to build and optimize models for their research and development work in a very short period of time. This saves them a lot of time and resources.
- Deployable Across A Wide Variety Of Platforms
Keras is also becoming more popular because it can be easily deployed across a wide variety of platforms. Models built on Keras, for instance, can be deployed on a server which runs through Python runtime.
Because of its widespread use in industry and the academe, Keras is already becoming a leading API among deep-learning frameworks. Keras is very flexible and can be deployed to a wide array of platforms. This flexibility allows it to be used in a variety of web and mobile applications. And because it only needs minimal programming skills, it has enabled explosive innovation and rapid software development.
- Highly Scalable And Built On TensorFlow As Backend
Despite being suited more for smaller data sets, Keras is highly scalable because it’s built on TensorFlow and can use the latter as its backend. Keras would be a great tool to use if you’re a start-up. You can design your models with small data sets while you’re still starting your business. This helps bring down the cost of training and testing data while your platform is still small.
But the good thing about Keras is that you can easily scale up your platform’s computational capacity as Keras provides native support of distributed computing. If you want to compare TensorFlow and Keras, you have to bear in mind that some of the functions of Keras serve as wrapper to TensorFlow, which is a Google-developed framework. TensorFlow is an end-to-end framework containing a library which can be used for multiple tasks in machine learning. Keras, on the other hand, is a high-level neural network used in deep learning.
TensorFlow Is To Machine Learning As Keras Is To Deep Learning
AI programmers who work with very large data sets that need high performance and functionality would prefer to use TensorFlow. Keras is more suited to not-so-large datasets when you want to do quick experiments and to develop your models into products in a shorter span of time.
