PyTorch is an open-source machine learning library for Python, based on Torch, used for applications such as natural language processing.
Tensorflow is based on Theano and has been developed by Google, whereas PyTorch is based on Torch and has been developed by Facebook.
The most important difference between the two is the way these frameworks define the computational graphs. While Tensorflow creates a static graph, PyTorch believes in a dynamic graph. So what does this mean? In Tensorflow, you first have to define the entire computation graph of the model and then run your ML model. But in PyTorch, you can define/manipulate your graph on-the-go. This is particularly helpful while using variable length inputs in RNNs.
- https://en.wikipedia.org/wiki/PyTorch
- https://medium.com/@UdacityINDIA/tensorflow-or-pytorch-the-force-is-strong-with-which-one-68226bb7dab4
What about Keras?