Pytorch is a deep learning framework for python, although it's still at the moment (2019) more used than tensorflow, it has a very interesting adoption curve amaong researchers.
In this tutorial we will go through the basics, and things which are not so clear in the common tutorials found on the web (at least not so clear for me).
Let's see in these notes how pytorch works.
I would say that most pytorch programs contain a:
- A way to Load the data to feed to the neural network (from now on the dataloader)
- A Model of the Neural Network (from now on, the model)
- A Loss function to use in the backpropagation algorithm (from now on, the loss)
- An Optimizer for the Neural Network (from now on, the optimizer)
- A bunch of other neural network tuning variables (from now on, the hyperparameters)
Of course, these are only basic elements, then of course we have the training and validation phase or we can for example have regularization blocks or more advanced stuff.
In this tutorial, we will try to analyze each of these blocks, in order to be able to use pytorch for most of the problems.