# PyTorch


<!-- WARNING: THIS FILE WAS AUTOGENERATED! DO NOT EDIT! -->

PyTorch tensors are like NumPy arrays, but with a couple of extensions

1.  GPU placement — tensors can be moved to a GPU for accelerated
    computation
2.  Autograd — tensors participate in a computational graph that enables
    backpropagation

### The Computational Graph

- PyTorch builds the graph dynamically as your code executes
  (define-by-run). Other frameworks like TensorFlow declare it upfront.
- Each operation on tracked tensors adds a node to the graph, recording
  how outputs were derived from inputs
- This is what makes `.backward()` possible: PyTorch traverses the graph
  in reverse to compute gradients

### requires_grad

- Graph tracking is **opt-in**: a tensor only participates if
  `requires_grad=True`
- Learnable parameters in a model have this set automatically

### torch.no_grad()

- During inference you don’t need gradients, so wrapping code in
  `torch.no_grad()` disables graph tracking
- This saves both memory and computation