I’m going through this tutorial on matrix decompositions from fast.ai and they use PyTorch in their examples. So I was curious as to what the differences were compared to other libraries I have heard of such as Keras and Tensorflow.
- Keras is high-level API to manage experimentation of neural networks of other libraries e.g. Tensorflow
- Tensorflow is Google’s general machine learning library that uses a “static computational graph” i.e. you define your model and then run it
- PyTorch is a “dynamic computational graph”, where you can have any number of inputs throughout the model and the model is modular so that you can debug parts of it at a time, but only works on Linux and macOS (more on PyTorch).
Edit: Some more comparisons between PyTorch and TensorFlow.