Answers.Body: <p>TensorFlow was developed by Google and is based on Theano (Python library), while Facebook developed PyTorch using the Torch library. Both frames are useful and have a great community behind them. Both provide machine learning libraries to accomplish various tasks and do the job. TensorFlow is a powerful and deep learning tool with active visualization and debugging capabilities. TensorFlow also offers serialization benefits since the entire graphic is saved as a protocol buffer. It also has support for mobile platforms and offers a production-ready implementation. PyTorch, on the other hand, is still gaining momentum and attracting Python developers, since it is more Python friendly. In summary, TensorFlow is used to speed things up and create AI-related products, while research-oriented developers prefer PyTorch.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-01-18T19:45:22.460Z
Answers.LastActivityDate: 2020-01-18T19:45:22.460Z
Answers.UserDisplayName: Hiren Namera
Body: <p>What are the differences between TensorFlow and PyTorch, both in terms of performance and functionality?</p>
  
Tags: <comparison><tensorflow><pytorch>
Title: What are the differences between TensorFlow and PyTorch?
UserDisplayName: Seyed Moein Ayyoubzadeh
ThreadText: # What are the differences between TensorFlow and PyTorch?
  
  (Asked by: username_0 on 2020-01-18)
  
  What are the differences between TensorFlow and PyTorch, both in terms of performance and functionality?
  
  **Tags:** comparison, tensorflow, pytorch
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2020-01-18. Score: 3 | Accepted answer
  
  TensorFlow was developed by Google and is based on Theano (Python library), while Facebook developed PyTorch using the Torch library. Both frames are useful and have a great community behind them. Both provide machine learning libraries to accomplish various tasks and do the job. TensorFlow is a powerful and deep learning tool with active visualization and debugging capabilities. TensorFlow also offers serialization benefits since the entire graphic is saved as a protocol buffer. It also has support for mobile platforms and offers a production-ready implementation. PyTorch, on the other hand, is still gaining momentum and attracting Python developers, since it is more Python friendly. In summary, TensorFlow is used to speed things up and create AI-related products, while research-oriented developers prefer PyTorch.
  
  
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