diff --git a/beginner_source/examples_autograd/polynomial_autograd.py b/beginner_source/examples_autograd/polynomial_autograd.py
index d33ca8bcb90..b8353fc598e 100755
--- a/beginner_source/examples_autograd/polynomial_autograd.py
+++ b/beginner_source/examples_autograd/polynomial_autograd.py
@@ -2,8 +2,8 @@
 PyTorch: Tensors and autograd
 -------------------------------
 
-A third order polynomial, trained to predict :math:`y=\sin(x)` from :math:`-\pi`
-to :math:`\pi` by minimizing squared Euclidean distance.
+A third order polynomial, trained to predict :math:`y=e^x` from :math:`-1`
+to :math:`1` by minimizing squared Euclidean distance.
 
 This implementation computes the forward pass using operations on PyTorch
 Tensors, and uses PyTorch autograd to compute gradients.
@@ -14,7 +14,6 @@
 holding the gradient of ``x`` with respect to some scalar value.
 """
 import torch
-import math
 
 # We want to be able to train our model on an `accelerator <https://pytorch.org/docs/stable/torch.html#accelerators>`__
 # such as CUDA, MPS, MTIA, or XPU. If the current accelerator is available, we will use it. Otherwise, we use the CPU.
@@ -39,7 +38,6 @@
 c = torch.randn((), dtype=dtype, requires_grad=True)
 d = torch.randn((), dtype=dtype, requires_grad=True)
 
-initial_loss = 1.
 learning_rate = 1e-5
 for t in range(5000):
     # Forward pass: compute predicted y using operations on Tensors.
@@ -50,7 +48,7 @@
     # loss.item() gets the scalar value held in the loss.
     loss = (y_pred - y).pow(2).sum()
 
-    # Calculare initial loss, so we can report loss relative to it
+    # Calculate initial loss, so we can report loss relative to it
     if t==0:
         initial_loss=loss.item()