diff --git a/.ipynb_checkpoints/Cifar10-checkpoint.ipynb b/.ipynb_checkpoints/Cifar10-checkpoint.ipynb
index eed1b45..83d472d 100644
--- a/.ipynb_checkpoints/Cifar10-checkpoint.ipynb
+++ b/.ipynb_checkpoints/Cifar10-checkpoint.ipynb
@@ -273,6 +273,7 @@
     }
    ],
    "source": [
+    "# Look at our class names\n",
     "class_names = np.array(dataset_info.features['label'].names)\n",
     "\n",
     "print(class_names)"
@@ -298,6 +299,7 @@
     }
    ],
    "source": [
+    "# Make some predictions\n",
     "image_batch, label_batch = next(iter(train_batches))\n",
     "\n",
     "\n",
@@ -330,6 +332,7 @@
     }
    ],
    "source": [
+    "# Computed actual labels to our predictions\n",
     "print(\"Labels:           \", label_batch)\n",
     "print(\"Predicted labels: \", predicted_ids)"
    ]
@@ -353,6 +356,7 @@
     }
    ],
    "source": [
+    "# Visualize our predictions\n",
     "plt.figure(figsize=(10,9))\n",
     "for n in range(30):\n",
     "    plt.subplot(6,5,n+1)\n",
@@ -390,22 +394,29 @@
    ],
    "source": [
     "# setup model with Inception V3 pretrained model as feature extractor\n",
+    "\n",
+    "# Set image resolution to math Inception V3 input shape\n",
     "IMAGE_RES = 299\n",
     "\n",
+    "# Load in data\n",
     "(training_set, validation_set), dataset_info = tfds.load(\n",
     "    'cifar10', \n",
     "    with_info=True, \n",
     "    as_supervised=True, \n",
     "    split=['train[:70%]', 'train[70%:]'],\n",
     ")\n",
+    "\n",
+    "# Split into training batches\n",
     "train_batches = training_set.shuffle(num_training_examples//4).map(format_image).batch(BATCH_SIZE).prefetch(1)\n",
     "validation_batches = validation_set.map(format_image).batch(BATCH_SIZE).prefetch(1)\n",
     "\n",
+    "# Grab our pretrained model and set as our feature extractor\n",
     "URL = \"https://tfhub.dev/google/tf2-preview/inception_v3/feature_vector/4\"\n",
     "feature_extractor = hub.KerasLayer(URL,\n",
     "  input_shape=(IMAGE_RES, IMAGE_RES, 3),\n",
     "  trainable=False)\n",
     "\n",
+    "# Add a prediction layer to our feature extractor\n",
     "model_inception = tf.keras.Sequential([\n",
     "  feature_extractor,\n",
     "  tf.keras.layers.Dense(num_classes)\n",
@@ -425,6 +436,7 @@
     "  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
     "  metrics=['accuracy'])\n",
     "\n",
+    "# Adjust Epochs to properly fit model\n",
     "EPOCHS = 1\n",
     "\n",
     "history = model_inception.fit(train_batches,\n",
diff --git a/Cifar10.ipynb b/Cifar10.ipynb
index eed1b45..83d472d 100644
--- a/Cifar10.ipynb
+++ b/Cifar10.ipynb
@@ -273,6 +273,7 @@
     }
    ],
    "source": [
+    "# Look at our class names\n",
     "class_names = np.array(dataset_info.features['label'].names)\n",
     "\n",
     "print(class_names)"
@@ -298,6 +299,7 @@
     }
    ],
    "source": [
+    "# Make some predictions\n",
     "image_batch, label_batch = next(iter(train_batches))\n",
     "\n",
     "\n",
@@ -330,6 +332,7 @@
     }
    ],
    "source": [
+    "# Computed actual labels to our predictions\n",
     "print(\"Labels:           \", label_batch)\n",
     "print(\"Predicted labels: \", predicted_ids)"
    ]
@@ -353,6 +356,7 @@
     }
    ],
    "source": [
+    "# Visualize our predictions\n",
     "plt.figure(figsize=(10,9))\n",
     "for n in range(30):\n",
     "    plt.subplot(6,5,n+1)\n",
@@ -390,22 +394,29 @@
    ],
    "source": [
     "# setup model with Inception V3 pretrained model as feature extractor\n",
+    "\n",
+    "# Set image resolution to math Inception V3 input shape\n",
     "IMAGE_RES = 299\n",
     "\n",
+    "# Load in data\n",
     "(training_set, validation_set), dataset_info = tfds.load(\n",
     "    'cifar10', \n",
     "    with_info=True, \n",
     "    as_supervised=True, \n",
     "    split=['train[:70%]', 'train[70%:]'],\n",
     ")\n",
+    "\n",
+    "# Split into training batches\n",
     "train_batches = training_set.shuffle(num_training_examples//4).map(format_image).batch(BATCH_SIZE).prefetch(1)\n",
     "validation_batches = validation_set.map(format_image).batch(BATCH_SIZE).prefetch(1)\n",
     "\n",
+    "# Grab our pretrained model and set as our feature extractor\n",
     "URL = \"https://tfhub.dev/google/tf2-preview/inception_v3/feature_vector/4\"\n",
     "feature_extractor = hub.KerasLayer(URL,\n",
     "  input_shape=(IMAGE_RES, IMAGE_RES, 3),\n",
     "  trainable=False)\n",
     "\n",
+    "# Add a prediction layer to our feature extractor\n",
     "model_inception = tf.keras.Sequential([\n",
     "  feature_extractor,\n",
     "  tf.keras.layers.Dense(num_classes)\n",
@@ -425,6 +436,7 @@
     "  loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
     "  metrics=['accuracy'])\n",
     "\n",
+    "# Adjust Epochs to properly fit model\n",
     "EPOCHS = 1\n",
     "\n",
     "history = model_inception.fit(train_batches,\n",