Update solutions and exercises to be equivalent

Author: ma595

exercises/01_penguin_classification.ipynb

@@ -37,7 +37,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Task 1 -- Part (a): look at the data\n",
+    "### Task 1 -- Part (a): Look at the data\n",
     "In the following code block, we import the ``load_penguins`` function from the ``palmerpenguins`` package.\n",
     "\n",
     "- Call this function, which returns a single object, and assign it to the variable ``data``.\n",
@@ -79,7 +79,18 @@
    "outputs": [],
    "source": [
     "# import seaborn as sns\n",
-    "# sns.pairplot(data.drop(\"year\", axis=1), hue='species')"
+    "\n",
+    "# species_palette = {\n",
+    "#     \"Adelie\": sns.color_palette()[0],     # Blue\n",
+    "#     \"Chinstrap\": sns.color_palette()[1],  # Orange\n",
+    "#     \"Gentoo\": sns.color_palette()[2],     # Green\n",
+    "# }\n",
+    "\n",
+    "# sns.pairplot(\n",
+    "#     data.drop(\"year\", axis=1),\n",
+    "#     hue=\"species\",\n",
+    "#     palette=species_palette,\n",
+    "#     hue_order=[\"Adelie\", \"Chinstrap\", \"Gentoo\"],  "
    ]
   },
   {
@@ -122,14 +133,12 @@
     "reducer = umap.UMAP(random_state=42)\n",
     "embedding = reducer.fit_transform(scaled_penguin_data)\n",
     "\n",
-    "colors = sns.color_palette()\n",
-    "\n",
     "for i, (species, group) in enumerate(data.groupby(\"species\")):\n",
     "    plt.scatter(\n",
     "        embedding[data.species == species, 0],\n",
     "        embedding[data.species == species, 1],\n",
     "        label=species,\n",
-    "        color=colors[i],\n",
+    "        color=species_palette[species],\n",
     "    )\n",
     "\n",
     "plt.gca().set_aspect(\"equal\", \"datalim\")\n",
@@ -557,7 +566,7 @@
     "# Print the model architecture.\n",
     "# print(res_model)\n",
     "\n",
-    "list(res_model.parameters())"
+    "# list(res_model.parameters())"
    ]
   },
   {
@@ -795,14 +804,14 @@
     "\n",
     "        # zero the gradients (otherwise gradients accumulate)\n",
     "\n",
-    "        # run forward model and compute proxy probabilities over dimension 1 (columns of tensor).\n",
+    "        # run forward model to make predictions\n",
     "\n",
     "        # compute loss\n",
     "        # e.g. pred : Tensor([3]) and target : int\n",
     "\n",
     "        # compute gradients\n",
     "\n",
-    "        # nudge parameters in direction of steepest descent c\n",
+    "        # nudge parameters in direction of steepest descent\n",
     "\n",
     "        # append metrics\n",
     "\n",