some more -typos

03_pytorch_computer_vision.ipynb

@@ -2163,7 +2163,7 @@
     "\n",
     "Overfitting means our model is learning the training data well but those patterns aren't generalizing to the testing data.\n",
     "\n",
-    "Two of the main to fix overfitting include:\n",
+    "Two of the main ways to fix overfitting include:\n",
     "1. Using a smaller or different model (some models fit certain kinds of data better than others).\n",
     "2. Using a larger dataset (the more data, the more chance a model has to learn generalizable patterns).\n",
     "\n",
@@ -2306,7 +2306,7 @@
     "        self.classifier = nn.Sequential(\n",
     "            nn.Flatten(),\n",
     "            # Where did this in_features shape come from? \n",
-    "            # It's because each layer of our network compresses and changes the shape of our inputs data.\n",
+    "            # It's because each layer of our network compresses and changes the shape of our input data.\n",
     "            nn.Linear(in_features=hidden_units*7*7, \n",
     "                      out_features=output_shape)\n",
     "        )\n",
@@ -3014,7 +3014,7 @@
    "source": [
     "Notice the change in the shapes of what's happening in and out of a `nn.MaxPool2d()` layer.\n",
     "\n",
-    "The `kernel_size` of the `nn.MaxPool2d()` layer will effects the size of the output shape.\n",
+    "The `kernel_size` of the `nn.MaxPool2d()` layer will affect the size of the output shape.\n",
     "\n",
     "In our case, the shape halves from a `62x62` image to `31x31` image.\n",
     "\n",
@@ -3050,7 +3050,7 @@
    ],
    "source": [
     "torch.manual_seed(42)\n",
-    "# Create a random tensor with a similiar number of dimensions to our images\n",
+    "# Create a random tensor with a similar number of dimensions to our images\n",
     "random_tensor = torch.randn(size=(1, 1, 2, 2))\n",
     "print(f\"Random tensor:\\n{random_tensor}\")\n",
     "print(f\"Random tensor shape: {random_tensor.shape}\")\n",