diff --git a/notebooks/1_visualize_text.ipynb b/notebooks/1_visualize_text.ipynb
index d7a9690..4084840 100644
--- a/notebooks/1_visualize_text.ipynb
+++ b/notebooks/1_visualize_text.ipynb
@@ -225,6 +225,10 @@
    "source": [
     "# lambdas = eigenvalues\n",
     "print(kpca.lambdas_[:10])\n",
+    "# check how much \"information\" we would keep if we were to reduce the dimensionality to 20\n",
+    "# (this is not 100% accurate, since we only computed the first 100 kPCA components, i.e.,\n",
+    "# normally lambda_ should contain all eigenvalues - but this should be close enough)\n",
+    "print(\"Percentage of variance retained with 20 components:\", 100*(sum(kpca.lambdas_[:20])/sum(kpca.lambdas_)))\n",
     "# plot eigenvalue spectrum\n",
     "plt.figure()\n",
     "plt.plot(range(1, len(kpca.lambdas_)+1), kpca.lambdas_)\n",
diff --git a/notebooks/2_image_quantization.ipynb b/notebooks/2_image_quantization.ipynb
index bc17d00..5b4b932 100644
--- a/notebooks/2_image_quantization.ipynb
+++ b/notebooks/2_image_quantization.ipynb
@@ -54,7 +54,8 @@
    "source": [
     "# reshape image into a matrix with RGB values for each pixel\n",
     "h, w, d = img_array.shape\n",
-    "X = ...  # TODO: reshape img_array such that X is a matrix of shape n_pixels x 3 RGB channels\n",
+    "X = new_X.reshape(h*w, d)\n",
+    "# 1 pixel = 1 data point; RGB values = features\n",
     "print(X.shape)"
    ]
   },
@@ -122,7 +123,7 @@
    "outputs": [],
    "source": [
     "# reshape back into image format\n",
-    "img_new = new_X.reshape(h, w, d)\n",
+    "img_new = ... # TODO: reshape new_X such that img_new is a matrix of shape height x width x 3 RGB channels\n",
     "print(img_new.shape)"
    ]
   },