diff --git a/.gitignore b/.gitignore
index 7df4764..c8cffdf 100644
--- a/.gitignore
+++ b/.gitignore
@@ -193,3 +193,8 @@ ipython_config.py
 data/**
 !data/**/
 !**/.gitkeep
+
+
+
+models/**
+!**/.gitkeep
diff --git a/models/.gitkeep b/models/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/notebooks/tutorial-cnn.ipynb b/notebooks/tutorial-cnn.ipynb
index f490625..945c9f9 100644
--- a/notebooks/tutorial-cnn.ipynb
+++ b/notebooks/tutorial-cnn.ipynb
@@ -359,6 +359,80 @@
     "# SGD is used on tutorial, but Adam optimizer is more popular as well\n",
     "optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9) # Defualt learning rate from tutorial: compromise btwn overshoot and training infinitely long, 0.9 --> 90% of update comes from prev. direction\n"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "572d80e3",
+   "metadata": {},
+   "source": [
+    "Tutorial training cell adapted to use 10 epochs "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "374d0590",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1: Loss=1.575, Accuracy=50.54%\n",
+      "Epoch 2: Loss=1.196, Accuracy=60.35%\n",
+      "Epoch 3: Loss=1.018, Accuracy=64.98%\n",
+      "Epoch 4: Loss=0.909, Accuracy=68.58%\n",
+      "Epoch 5: Loss=0.842, Accuracy=70.87%\n",
+      "Epoch 6: Loss=0.784, Accuracy=72.83%\n",
+      "Epoch 7: Loss=0.738, Accuracy=74.24%\n",
+      "Epoch 8: Loss=0.703, Accuracy=75.30%\n",
+      "Epoch 9: Loss=0.671, Accuracy=76.30%\n",
+      "Epoch 10: Loss=0.641, Accuracy=77.64%\n",
+      "Finished Training\n"
+     ]
+    }
+   ],
+   "source": [
+    "for epoch in range(10): # 10 epochs\n",
+    "    running_loss = 0.0\n",
+    "    correct = 0\n",
+    "    total = 0\n",
+    "\n",
+    "    for i, data in enumerate(train_loader, 0):\n",
+    "        inputs, labels = data\n",
+    "        inputs, labels = inputs.to(device), labels.to(device)  # use GPU\n",
+    "\n",
+    "        optimizer.zero_grad()\n",
+    "\n",
+    "        outputs = model(inputs)\n",
+    "        loss = criterion(outputs, labels)\n",
+    "        loss.backward()\n",
+    "        optimizer.step()\n",
+    "\n",
+    "        # loss tracking\n",
+    "        running_loss += loss.item()\n",
+    "\n",
+    "        # accuracy tracking\n",
+    "        _, predicted = torch.max(outputs, 1)\n",
+    "        total += labels.size(0)\n",
+    "        correct += (predicted == labels).sum().item()\n",
+    "\n",
+    "    accuracy = 100 * correct / total\n",
+    "    print(f'Epoch {epoch + 1}: Loss={running_loss / len(train_loader):.3f}, Accuracy={accuracy:.2f}%')  # Credit: Claude for printing accuracy in good formatting\n",
+    "\n",
+    "print('Finished Training')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2bf2b9a2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "PATH = '../models/tutorial-cnn.pth'\n",
+    "torch.save(model.state_dict(), PATH)"
+   ]
   }
  ],
  "metadata": {