diff --git a/notebooks/examples/single-layer_perceptron.ipynb b/notebooks/examples/single-layer_perceptron.ipynb
index 200fb71606760518eb1ca4fbbcb08c09a0700487..ac1f62d6f9d01467ac8ed04d206e971bc1d71a33 100644
--- a/notebooks/examples/single-layer_perceptron.ipynb
+++ b/notebooks/examples/single-layer_perceptron.ipynb
@@ -5,7 +5,9 @@
    "id": "7a974be2",
    "metadata": {},
    "source": [
-    "# Single-Layer Perceptron"
+    "# Single-Layer Perceptron\n",
+    "In this notebook, we will implement a single-layer perceptron.\n",
+    "Goal is to predict an or-gate."
    ]
   },
   {
@@ -124,14 +126,15 @@
    "source": [
     "def predict(inputs, weights):\n",
     "  '''inputs: matrix[i][:-1], weights: array of weights'''\n",
-    "\n",
+    "  \n",
     "  total_activation = 0\n",
     "  # Iterate through the inputs and related weights and sum them up in total_activation\n",
     "  for input, weight in zip(inputs, weights):\n",
     "    total_activation += input * weight\n",
     "  \n",
     "  # calculate sigmoid activation to limit the output between 0 and 1\n",
-    "  return 1/(1+math.exp(-total_activation))"
+    "  return 1/(1+math.exp(-total_activation))\n",
+    "  "
    ]
   },
   {
@@ -189,15 +192,16 @@
     "def accuracy(matrix, weights):\n",
     "  num_correct = 0\n",
     "  preds = []\n",
-    "  threshold = 0.4\n",
     "  \n",
     "  for i in range(len(matrix)):\n",
     "    # Get prediction for the current datapoint\n",
     "    pred = predict(matrix[i][:-1], weights)\n",
     "    preds.append(pred)\n",
     "\n",
-    "    # Check if the prediction is correct within the threshold\n",
-    "    if math.isclose(pred, matrix[i][-1], abs_tol = threshold):\n",
+    "    # Check if the prediction is correct\n",
+    "    # math.isclose checks if two values are close to each other\n",
+    "    # abs_tol is the minimum absolute tolerance.\n",
+    "    if math.isclose(pred, matrix[i][-1], abs_tol = 0.4):\n",
     "        num_correct += 1\n",
     "        \n",
     "  print('Predictions:', preds)\n",
diff --git a/notebooks/exercises/solution_single-layer_perceptron.ipynb b/notebooks/exercises/solution_single-layer_perceptron.ipynb
index 4447000c299d78adfc462491b1d3218847462cf2..59f6b207f47b219859273e050f93686ece9106b7 100644
--- a/notebooks/exercises/solution_single-layer_perceptron.ipynb
+++ b/notebooks/exercises/solution_single-layer_perceptron.ipynb
@@ -100,10 +100,14 @@
    "outputs": [],
    "source": [
     "def predict(inputs, weights):\n",
-    "  threshold = 0.5\n",
+    "  '''inputs: matrix[i][:-1], weights: array of weights'''\n",
+    "  \n",
     "  total_activation = 0\n",
+    "  # Iterate through the inputs and related weights and sum them up in total_activation\n",
     "  for input, weight in zip(inputs, weights):\n",
     "    total_activation += input * weight\n",
+    "  \n",
+    "  # calculate sigmoid activation to limit the output between 0 and 1\n",
     "  return 1/(1+math.exp(-total_activation))"
    ]
   },
@@ -153,13 +157,20 @@
     "def accuracy(matrix, weights):\n",
     "  num_correct = 0\n",
     "  preds = []\n",
+    "  \n",
     "  for i in range(len(matrix)):\n",
+    "    # Get prediction for the current datapoint\n",
     "    pred = predict(matrix[i][:-1], weights)\n",
     "    preds.append(pred)\n",
-    "    #if pred ==  matrix[i][-1]:\n",
+    "\n",
+    "    # Check if the prediction is correct\n",
+    "    # math.isclose checks if two values are close to each other\n",
+    "    # abs_tol is the minimum absolute tolerance.\n",
     "    if math.isclose(pred, matrix[i][-1], abs_tol = 0.4):\n",
     "        num_correct += 1\n",
+    "        \n",
     "  print('Predictions:', preds)\n",
+    "\n",
     "  # return overall accuracy\n",
     "  return num_correct / float(len(matrix))"
    ]