Fast AI's Pytorch tutorial

Fast AI wrote a really nice pytorch tutorial which starts from a very manual implementation of a one hidden layer neural network trained on MNIST data, then gradually adds on pytorch built-in capabilities. It nicely shows off what pytorch can do, and how it simplifies the coding. The different steps involve using:

• torch.nn.functional: provides already built-in function for most common activation functions and loss functions.
• nn.Module: provides a class to define a neural network that can be inherited when defining your own NN.
• nn.Linear: already defines a linear layer (also have convolution layers,….)
• optim: provides a broad selection of optimizer that can be used to train your neural network. You still need to compute the gradient yourself, and zero out the gradient after the update step. The parameters of the NN are passed to the optimizer when being instantiated, which, in addition to telling the optimizer what parameters to optimize, also provide the optimizer with gradient information.
• Dataset: provides a convenient way to manipulate datasets; allows to slice train and test sets together.
• DataLoader: manages mini-batches automatically; you just give it a Dataset and a batch size.
• conv2d: pytorch has a lot of layers already built-in that can be directly used when building an object of type nn.Module.
• nn.Sequential: it is another way of defining a model, instead of nn.Module.