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Do you know that nowadays, modern technologies are in the race to solve the most complex human problems?
You must be very well aware as you are reading this article. Out of all these modern technologies, deep learning has a different fan base and following. Deep learning tries to solve all these problems by simulating human brain functioning using neural networks. However, the growing popularity also attracts comparison with competing libraries, like "PyTorch vs. TensorFlow" which is the better of the two.
In this age of digitization of data, deep learning is growing in its scope incessantly, from self-driving cars to solving complex puzzles of the human brain and body, from the entertainment industry to virtual assistants, and from chatbots to banking services. Therefore, machine learning technologies like deep learning have started to become omnipresent.
Hence tech giants like Facebook, Google, Tesla, Uber, and many more are working to make the most out of deep learning. TensorFlow and PyTorch are two major Python deep learning frameworks that came out of this hustle.
What is TensorFlow?
TensorFlow is a free, and open-source library based on Python. It is mainly used for developing deep learning applications especially those related to machine learning (ML) and artificial intelligence (AI).
What is PyTorch?
PyTorch is also an open-source and free framework based on the Torch library. It offers greater flexibility and increased speed for deep neural network implementation.
Now after understanding the applications and use cases of PyTorch and TensorFlow in deep learning, let’s try to understand which is the best deep learning framework-
Computational graph construction takes a different track in both. On one hand, it is static for TensorFlow, and on the other dynamic for PyTorch.
RESULT:
PyTorch is a clear winner when it comes to computational graph construction.
Serialization is the process in which the entire graph can be saved as a protocol buffer. Serialization includes API work, cross-language support, and functioning among others to work more efficiently.
RESULT:
TensorFlow wins the serialization race due to the wide range of services and functionalities it provides.
Debugging is essential to finding what exactly is breaking the code. And, like multiple other Python tools, TensorFlow also provides different classes and packages to make this simpler. Let’s analyze PyTorch and TensorFlow from this aspect
RESULT:
PyTorch is a clear winner here as well. This is because you don't have to put any extra effort into debugging.
Comparison for a more clear picture of which way to go.
While the above discussion and comparison might have put you in a dilemma as to which framework to choose, to make your life easier, let's have a look at these 4 crucial keys:
I hope the picture is much clearer now. To wrap up, no framework can be tagged as a complete solution for your deep learning needs. That goes the same for PyTorch and TensorFlow. It is the utility, functionality, project scope, interest, and expertise that should be looked into before reaching a final decision. Hence, just compare the scope, your requirements, and your interest before making a solid decision. That’s all from the PyTorch vs TensorFlow debate.
Additionally, don’t forget to tell us which way you did choose to go and why. We would like to hear your thoughts. Plus, feel free to suggest something that we missed. If you're an expert Python dev looking for some awesome new gig try remote work through Turing.
Abhishek is a Geek by day and Batman by night. He loves to talk about Data and his passion encircles around Trekking, Hitch Hiking, Gardening, and analyzing Ancient Indian Texts. His geeky stuff got highlighted at Microsoft, Code Project, C-sharp Corner, etc.