Fix typo (#1786)

Author: decoherencer

beginner_source/basics/optimization_tutorial.py

@@ -135,7 +135,7 @@ def forward(self, x):
 #####################################
 # Inside the training loop, optimization happens in three steps:
 #  * Call ``optimizer.zero_grad()`` to reset the gradients of model parameters. Gradients by default add up; to prevent double-counting, we explicitly zero them at each iteration.
-#  * Backpropagate the prediction loss with a call to ``loss.backwards()``. PyTorch deposits the gradients of the loss w.r.t. each parameter.
+#  * Backpropagate the prediction loss with a call to ``loss.backward()``. PyTorch deposits the gradients of the loss w.r.t. each parameter.
 #  * Once we have our gradients, we call ``optimizer.step()`` to adjust the parameters by the gradients collected in the backward pass.