Comparing PyTorch to other deep learning frameworks
PyTorch is one of the main frameworks used in deep learning today. There are other widely used frameworks available too, such as TensorFlow, Theano, and Caffe. While these are very similar in many ways, there are some key differences in how they operate. These include the following:
- How the models are computed
- The way in which the computational graphs are compiled
- The ability to create dynamic computational graphs with variable layers
- Differences in syntax
Arguably, the main difference between PyTorch and other frameworks is in the way that the models themselves are computed. PyTorch uses an automatic differentiation method called autograd, which allows computational graphs to be defined and executed dynamically. This is in contrast to other frameworks such as TensorFlow, which is a static framework. In these static frameworks, computational graphs must be defined and compiled before finally being executed...
