What is PyTorch's autograd feature?

What is PyTorch's autograd feature?

PyTorch's autograd is a powerful automatic differentiation library that helps in computing gradients for tensor operations.

How does it work in the background?

It works by creating a computational graph dynamically, where nodes represent operations and edges represent tensors.

Can you give me an example of how to use it?

Sure! You can use it by setting requires_grad=True on a tensor, and then calling .backward() on the output tensor.

What are the benefits of using autograd?

The benefits include ease of use, efficiency in computing gradients, and the ability to handle complex models seamlessly.