This repository walks through PyTorch’s core features through a hands-on running example: training a fully-connected ReLU network with a single hidden layer using gradient descent to fit random data. It covers tensors, automatic differentiation, nn modules, optimizers, and custom autograd functions. It also includes comparable numpy and TensorFlow implementations for context.