Author: Justin Johnson
This tutorial introduces the fundamental concepts of PyTorch through self-contained examples.
At its core, PyTorch provides two main features:
- An n-dimensional Tensor, similar to numpy but can run on GPUs
- Automatic differentiation for building and training neural networks
We will use a fully-connected ReLU network as our running example. The network will have a single hidden layer, and will be trained with gradient descent to fit random data by minimizing the Euclidean distance between the network output and the true output.