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Keras, TensorFlow and PyTorch are among the top three frameworks that are preferred by Data Scientists as well as beginners in the field of Deep Learning.This comparison on Keras vs TensorFlow vs PyTorch will provide you with a crisp knowledge about the top Deep Learning Frameworks and help you find out which one is suitable for you. In this blog you will get a complete insight into the above three frameworks in the following sequence:
TensorFlow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library that is used for machine learning applications like neural networks.
PyTorch is an open source machine learning library for Python, based on Torch. It is used for applications such as natural language processing and was developed by Facebook’s AI research group.
TensorFlow is a framework that provides both high and low level APIs. Pytorch, on the other hand, is a lower-level API focused on direct work with array expressions. It has gained immense interest in the last year, becoming a preferred solution for academic research, and applications of deep learning requiring optimizing custom expressions.
With the increasing demand in the field of Data Science, there has been an enormous growth of Deep learning technology in the industry. With this, all the three frameworks have gained quite a lot of popularity. Keras tops the list followed by TensorFlow and PyTorch. It has gained immense popularity due to its simplicity when compared to the other two.
These were the parameters that distinguish all the three frameworks but there is no absolute answer to which one is better. The choice ultimately comes down to
Now coming to the final verdict of Keras vs TensorFlow vs PyTorch let’s have a look at the situations that are most preferable for each one of these three deep learning frameworks
Keras is most suitable for:
PyTorch is most suitable for:
Now with this, we come to an end of this comparison on Keras vs TensorFlow vs PyTorch. I Hope you guys enjoyed this article and understood which Deep Learning Framework is most suitable for you.
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