Difference between PyTorch and TensorFlow

There are various deep learning libraries but the two most famous libraries are PyTorch and Tensorflow. Though both are open source libraries but sometime it becomes difficult to figure out the difference between the two. They are extensively used in commercial code and academic research.  

PyTorch:

It is an open-source library used in machine learning. It was developed by Facebook and was released to the public for the first time in 2016. It is imperative which means it runs immediately and the user can check if it is working or not before writing the full code. We can write a part of code and check it in real time, it is built-in python based implementation to provide compatibility as a deep learning platform. It rapidly gained users because of its user-friendly interface, which made the Tensorflow team acquire its popular features in Tensorflow 2.0.    

TensorFlow:

Just like PyTorch, it is also an open-source library used in machine learning. It was developed by Google and was released in 2015. Its name itself expresses how you can perform and organize tasks on data. Production and research are the main uses of Tensorflow. Neural networks mostly use Tensorflow to develop machine learning applications.

PyTorch V/S TensorFlow 

 Conclusion

It cannot be said that one library is good and one is bad, both are very useful frameworks and are used on a large scale. Both are machine learning libraries which are used to do various tasks. Tensorflow is a useful tool with debugging capabilities and visualization, It also saves graph as a protocol buffer. On the other hand Pytorch is still getting momentum and tempting python developers because of it’s friendly usage. In nutshell Tensorflow is used to automate things faster and make artificial intelligence related products whereas developers which are more research oriented prefer using Pytorch.