diff --git a/tutorials/W2D4_Macrolearning/W2D4_Tutorial3.ipynb b/tutorials/W2D4_Macrolearning/W2D4_Tutorial3.ipynb
index 490c9aaa0..f8f2813ba 100644
--- a/tutorials/W2D4_Macrolearning/W2D4_Tutorial3.ipynb
+++ b/tutorials/W2D4_Macrolearning/W2D4_Tutorial3.ipynb
@@ -58,16 +58,12 @@
     "# @title Tutorial slides\n",
     "# @markdown These are the slides for the videos in all tutorials today\n",
     "\n",
-    "from IPython.display import IFrame\n",
-    "from ipywidgets import widgets\n",
-    "out = widgets.Output()\n",
+    "from IPython.display import IFrame, display\n",
     "\n",
     "link_id = \"t36w8\"\n",
     "\n",
-    "with out:\n",
-    "    print(f\"If you want to download the slides: https://osf.io/download/{link_id}/\")\n",
-    "    display(IFrame(src=f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\", width=730, height=410))\n",
-    "display(out)"
+    "print(f\"If you want to download the slides: https://osf.io/download/{link_id}/\")\n",
+    "display(IFrame(src=f\"https://mfr.ca-1.osf.io/render?url=https://osf.io/{link_id}/?direct%26mode=render%26action=download%26mode=render\", width=730, height=410))"
    ]
   },
   {
@@ -160,7 +156,7 @@
     "logging.getLogger('matplotlib.font_manager').disabled = True\n",
     "\n",
     "%matplotlib inline\n",
-    "%config InlineBackend.figure_format = 'retina' # perfrom high definition rendering for images and plots\n",
+    "%config InlineBackend.figure_format = 'retina' # perform high definition rendering for images and plots\n",
     "plt.style.use(\"https://raw.githubusercontent.com/NeuromatchAcademy/course-content/main/nma.mplstyle\")"
    ]
   },
@@ -444,7 +440,7 @@
     "\n",
     "    def __len__(self):\n",
     "        \"\"\"Calculate the length of the dataset. It is obligatory for PyTorch to know in advance how many samples to expect (before training),\n",
-    "        thus we enforced to icnlude number of epochs and tasks per epoch in `FruitSupplyDataset` parameters.\"\"\"\n",
+    "        thus we enforced to include number of epochs and tasks per epoch in `FruitSupplyDataset` parameters.\"\"\"\n",
     "\n",
     "        return self.num_epochs * self.num_tasks\n",
     "\n",
@@ -758,7 +754,7 @@
     "\n",
     "    def __len__(self):\n",
     "        \"\"\"Calculate the length of the dataset. It is obligatory for PyTorch to know in advance how many samples to expect (before training),\n",
-    "        thus we enforced to icnlude number of epochs and tasks per epoch in `FruitSupplyDataset` parameters.\"\"\"\n",
+    "        thus we enforced to include number of epochs and tasks per epoch in `FruitSupplyDataset` parameters.\"\"\"\n",
     "\n",
     "        return self.num_epochs * self.num_tasks\n",
     "\n",