diff --git a/intro-to-pytorch/Part 2 - Neural Networks in PyTorch (Exercises).ipynb b/intro-to-pytorch/Part 2 - Neural Networks in PyTorch (Exercises).ipynb
index 4b0c645ace..38ffc232e0 100644
--- a/intro-to-pytorch/Part 2 - Neural Networks in PyTorch (Exercises).ipynb	
+++ b/intro-to-pytorch/Part 2 - Neural Networks in PyTorch (Exercises).ipynb	
@@ -333,7 +333,7 @@
    "source": [
     "### Activation functions\n",
     "\n",
-    "So far we've only been looking at the softmax activation, but in general any function can be used as an activation function. The only requirement is that for a network to approximate a non-linear function, the activation functions must be non-linear. Here are a few more examples of common activation functions: Tanh (hyperbolic tangent), and ReLU (rectified linear unit).\n",
+    "So far we've only been looking at the sigmoid activation function, but in general any function can be used as an activation function. The only requirement is that for a network to approximate a non-linear function, the activation functions must be non-linear. Here are a few more examples of common activation functions: Tanh (hyperbolic tangent), and ReLU (rectified linear unit).\n",
     "\n",
     "<img src=\"assets/activation.png\" width=700px>\n",
     "\n",