diff --git a/exercises/01_penguin_classification.ipynb b/exercises/01_penguin_classification.ipynb
index a80035f..05adb52 100644
--- a/exercises/01_penguin_classification.ipynb
+++ b/exercises/01_penguin_classification.ipynb
@@ -342,7 +342,7 @@
     "\n",
     "  The module `torch.nn` contains different classes that help you build neural network models. All models in PyTorch inherit from the subclass `nn.Module`, which has useful methods like `parameters()`, `__call__()` and others.\n",
     "\n",
-    "  `torch.nn` also has various layers that you can use to build your neural network. For example, we will use `nn.Linear` in our code below, which constructs a fully connected layer. In particular, we will two `nn.Linear` layers as part of our network in the `__init__()` method. `torch.nn.Linear` is a subclass of `torch.nn.Module`. \n",
+    "  `torch.nn` also has various layers that you can use to build your neural network. For example, we will use `nn.Linear` in our code below, which constructs a fully connected layer. `torch.nn.Linear` is a subclass of `torch.nn.Module`. \n",
     "\n",
     "  What exactly is a \"layer\"? It is essentially a step in the neural network computation. i.e. The `nn.Linear` layer computes the linear transformation of the input vector `$x$`:  `$y$ = $W^T x + b$`. Where `W` is the matrix of tunable parameters and `b` is a bias vector.\n",
     "\n",
@@ -384,13 +384,6 @@
     "# define a model and print and test (try with torch.rand() function)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that in a fully-connected feed-forward network, the number of units in each layer always decreases. The neural network is forced to condense information, step-by-step, until it computes the target output we desire. When solving prediction problems, we will rarely (if ever) have a later layer have more neurons than a previous layer."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},