diff --git a/04_pytorch_custom_datasets.ipynb b/04_pytorch_custom_datasets.ipynb
index 1996eb45..8c85a585 100644
--- a/04_pytorch_custom_datasets.ipynb
+++ b/04_pytorch_custom_datasets.ipynb
@@ -2090,7 +2090,7 @@
     "        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*16*16,\n",
     "                      out_features=output_shape)\n",
     "        )\n",
@@ -2361,7 +2361,7 @@
     "        # 5. Optimizer step\n",
     "        optimizer.step()\n",
     "\n",
-    "        # Calculate and accumulate accuracy metric across all batches\n",
+    "        # Calculate and accumulate accuracy metrics across all batches\n",
     "        y_pred_class = torch.argmax(torch.softmax(y_pred, dim=1), dim=1)\n",
     "        train_acc += (y_pred_class == y).sum().item()/len(y_pred)\n",
     "\n",
@@ -2522,7 +2522,7 @@
     "\n",
     "To keep our experiments quick, we'll train our model for **5 epochs** (though you could increase this if you want).\n",
     "\n",
-    "As for an **optimizer** and **loss function**, we'll use `torch.nn.CrossEntropyLoss()` (since we're working with multi-class classification data) and `torch.optim.Adam()` with a learning rate of `1e-3` respecitvely.\n",
+    "As for an **optimizer** and **loss function**, we'll use `torch.nn.CrossEntropyLoss()` (since we're working with multi-class classification data) and `torch.optim.Adam()` with a learning rate of `1e-3` respectively.\n",
     "\n",
     "To see how long things take, we'll import Python's [`timeit.default_timer()`](https://docs.python.org/3/library/timeit.html#timeit.default_timer) method to calculate the training time."
    ]
@@ -2772,7 +2772,7 @@
    "source": [
     "### 8.1 How to deal with overfitting\n",
     "\n",
-    "Since the main problem with overfitting is that you're model is fitting the training data *too well*, you'll want to use techniques to \"reign it in\".\n",
+    "Since the main problem with overfitting is that your model is fitting the training data *too well*, you'll want to use techniques to \"reign it in\".\n",
     "\n",
     "A common technique of preventing overfitting is known as [**regularization**](https://ml-cheatsheet.readthedocs.io/en/latest/regularization.html).\n",
     "\n",