Orbits/Code/Background information I've learned.md

Things I have learned about PyTorch and Neural networks.

Building models

All model building in Pytorch is based on the following three steps

1. start by creating an object that extends the nn.Module base class
2. define layers as class attributes (sequential wrapper for ease of use)
3. implement the .forward() method

Each layer is just a predefined 'function'. Really, they are objects that extend the nn.Module base class. Thus each NN can act as a layer in another NN. For example, I reimplemented an upscaling layer in BasicNeuralNet2. (I picked up a lot of this info here.)[https://deeplizard.com/learn/video/k4jY9L8H89U]

Also, neural networks can return more than just a single output as long as the loss function that is used for optimization can consume both of them. Thus I could write two separate neural networks (such as for launch and partials), and then write a third NN that binds the two together.