This is a repo for practicing SGD with the fastai library. It models some madeup data as a quadratic function and then steps through, adjusting weights until it aproximates the data.

• All the steps for carrying out Stochastic Gradient Descent.
• How gradient calculation works in PyTorch (.requirese_grad and .backward(), and .grad).
• Even if you are using SGD, you still need to decide on the form of the function you are optimising for e.g. a linear equation, cubic equation.