What are TensorFlow and Keras?

 

What are Tensorflow and Keras?

There are plenty of good machine learning frameworks and libraries available now. Out of them, TensorFlow and Keras are very popular frameworks for machine learning and deep learning. Nowadays, Machine Learning is used in many fields like mathematical analysis, making predictions, Business Intelligence, computer vision systems, autonomous vehicles, robotics, and even medical applications. In the past, machine learning was complex, slow, and required a lot of programming even for a simple neural network. But, with the progressive growth and rise of frameworks and libraries such as TensorFlow and Keras have made the process lot easier and faster.

Today, anyone can run and train a machine learning model even on a mini computer like the Raspberry Pi. TensorFlow and Keras plays a big role in this rapidly developing technology. However you must be wondering what is TensorFlow and Keras are? Are those similar or different? Let's understand what are TensorFlow and Keras and how exactly we can use these tools.

What is TensorFlow?


Among all machine learning frameworks and libraries available, the undisputed queen is TensorFlow , which has established itself as the most popular library in Deep Learning. Right now, it would be hard to imagine talking about machine learning without mentioning TensorFlow.

TensorFlow is a library developed by Google Brain for its machine learning and deep neural network applications, released as open source software on November 9, 2015. TensorFlow is developed to meet the needs of systems capable of building and training neural networks to detect and decipher patterns and correlations, analogous to the learning and reasoning used by humans. It is currently used for both research and production of Google products, replacing the role of its closed source predecessor, DistBelief.

TensorFlow computations are expressed as stateful dataflow graphs. The name TensorFlow derives from the operations that such neural networks perform on multidimensional arrays of data. These multidimensional arrays are referred to as "tensors".

TensorFlow can run on multiple CPUs and GPUs . This library is also available on 64-bit Linux, macOS, and mobile platforms including Android and iOS. Not only that, TensorFlow JS enables to run machine learning models in the browser and train machine learning models using NodeJS.

What is Keras?


Keras is an open source neural network library written in Python . It has been mainly developed by Fran├žois Chollet, a Google engineer. Keras is a High-level API and developed with the goal to speed up the creation of neural networks. For this, Keras does not work as a standalone framework , but as an intuitive user interface (API) that allows access to various machine learning frameworks such as TensorFlow , CNTK, and Theano. 

Keras has been an integral part of the core TensorFlow API since TensorFlow 1.4 was released. However, this library continues to develop as standalone software to support other frameworks, such as Microsoft Cognitive Toolkit or Theano.

Conclusion

Now you know TensorFlow and Keras, two libraries that allow us to develop Machine Learning projects in a simple way. Both libraries have many differences. Truly, we cannot compare these tools and conclude that one is better than the other. Usage of these frameworks depends on the use case. However TensorFlow and Keras works together perfectly.

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